{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "#  Cell Counting\n",
                "\n",
                "Cell counting is a fundamental task found in many biological research and medical diagnostic processes. It underlies decisions in cell culture, drug development, and disease analysis. However, traditional manual cell counting methods are often time-consuming and prone to human error. This variability can hinder research progress and lead to inconsistencies across studies. \n",
                "\n",
                "Although cell counting machines exist, they are expensive and may not be readily available to all researchers. Automating cell counting using machine learning offers a powerful solution to this problem. ML-powered cell counters can quickly and accurately analyze large volumes of cell samples, freeing up researchers' time and minimizing inconsistencies.\n",
                "\n",
                "Ready to build your own cell counter and revolutionize your research efficiency? This tutorial equips you with the knowledge and skills to create a customized tool that streamlines your cell counting needs.\n",
                "\n",
                "\n",
                "## Colab\n",
                "\n",
                "This tutorial and the rest in this sequence can be done in Google colab. If you'd like to open this notebook in colab, you can use the following link.\n",
                "\n",
                "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepchem/deepchem/blob/master/examples/tutorials/Cell_Counting_Tutorial.ipynb)\n",
                "\n",
                "\n",
                "## Setup\n",
                "\n",
                "To run DeepChem within Colab, you'll need to run the following installation commands. You can of course run this tutorial locally if you prefer. In that case, don't run these cells since they will download and install DeepChem in your local machine again."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {
                "colab": {
                    "base_uri": "https://localhost:8080/",
                    "height": 278
                },
                "id": "qv54PkmBTeFT",
                "outputId": "31e85e0c-9771-42c1-f908-fe3c327f671c"
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Requirement already satisfied: deepchem in /usr/local/lib/python3.10/dist-packages (2.7.2.dev20240221173509)\n",
                        "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from deepchem) (1.3.2)\n",
                        "Requirement already satisfied: numpy>=1.21 in /usr/local/lib/python3.10/dist-packages (from deepchem) (1.25.2)\n",
                        "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from deepchem) (1.5.3)\n",
                        "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from deepchem) (1.2.2)\n",
                        "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from deepchem) (1.12)\n",
                        "Requirement already satisfied: scipy>=1.10.1 in /usr/local/lib/python3.10/dist-packages (from deepchem) (1.11.4)\n",
                        "Requirement already satisfied: rdkit in /usr/local/lib/python3.10/dist-packages (from deepchem) (2023.9.5)\n",
                        "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->deepchem) (2.8.2)\n",
                        "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->deepchem) (2023.4)\n",
                        "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from rdkit->deepchem) (9.4.0)\n",
                        "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->deepchem) (3.3.0)\n",
                        "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->deepchem) (1.3.0)\n",
                        "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->deepchem) (1.16.0)\n"
                    ]
                },
                {
                    "data": {
                        "application/vnd.google.colaboratory.intrinsic+json": {
                            "type": "string"
                        },
                        "text/plain": [
                            "'2.7.2.dev'"
                        ]
                    },
                    "execution_count": 16,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "!pip install --pre deepchem\n",
                "\n",
                "import deepchem as dc\n",
                "dc.__version__"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "id": "K_YeAVDoD2ew"
            },
            "source": [
                "Now we will import all the necessary packages and functions"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {
                "id": "jYO5qAI4T9SQ"
            },
            "outputs": [],
            "source": [
                "import numpy as np\n",
                "import matplotlib.pyplot as plt\n",
                "\n",
                "from deepchem.data import NumpyDataset\n",
                "from deepchem.models.torch_models import CNN\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## BBBC Datasets\n",
                "\n",
                " We used the image set [BBBC002v1](https://bbbc.broadinstitute.org/bbbc/BBBC002) [[Carpenter et al., Genome Biology, 2006](http://genomebiology.com/2006/7/10/R100)] from the Broad Bioimage Benchmark Collection [[Ljosa et al., Nature Methods, 2012](http://dx.doi.org/10.1038/nmeth.2083)] for this tutorial.\n",
                "\n",
                "The Broad Bioimage Benchmark Collection Dataset 002 (BBBC002) contains images of Drosophila Kc167 cells. The ground truth labels consist of cell counts.\n",
                "Full details about this dataset are present at https://bbbc.broadinstitute.org/BBBC002.\n",
                "\n",
                "For counting cells, our dataset needs to have images as inputs and the corresponding cell counts as the ground truth labels. We have several BBBC datasets that can be loaded using the deepchem package. These datasets are an extension to [`MoleculeNet`](https://moleculenet.org/) and can be accessed through [`dc.molnet`](https://deepchem.readthedocs.io/en/latest/api_reference/moleculenet.html).\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "id": "QszGtW_3D8yf"
            },
            "source": [
                "The `BBBC002` dataset consists of 60 images, each 512x512 pixels in size, which are split into train, validation and test sets in a 80/10/10 split by default. \\\n",
                "We also use `splitter='random'` in order to ensure that these images are randomly split into the train, validation and test sets in the above mention ratios.  "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {
                "id": "zmwNyJe4UWW9"
            },
            "outputs": [],
            "source": [
                "bbbc2_dataset = dc.molnet.load_bbbc002(splitter='random')\n",
                "tasks, dataset, transforms = bbbc2_dataset\n",
                "train, val, test = dataset\n",
                "\n",
                "train_x, train_y, train_w, train_ids = train.X, train.y, train.w, train.ids\n",
                "val_x, val_y, val_w, val_ids = val.X, val.y, val.w, val.ids\n",
                "test_x, test_y, test_w, test_ids = test.X, test.y, test.w, test.ids\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "id": "5bt6j-GLILyI"
            },
            "source": [
                "Now that we've loaded the dataset and randomly split it, let's take a look at the data."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {
                "colab": {
                    "base_uri": "https://localhost:8080/"
                },
                "id": "Tfzph9NNILEC",
                "outputId": "150575af-48b5-444f-c679-5173c76e1892"
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Shape of train data: (40, 512, 512)\n",
                        "Shape of train labels: (40,)\n"
                    ]
                }
            ],
            "source": [
                "print(f\"Shape of train data: {train_x.shape}\")\n",
                "print(f\"Shape of train labels: {train_y.shape}\")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "We can confirm that a sample from our dataset is in the form of a 512x512 image. Let's visualize this sample:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {
                "colab": {
                    "base_uri": "https://localhost:8080/",
                    "height": 468
                },
                "id": "Ez5HmhKWUqRI",
                "outputId": "b91f7811-a745-41eb-ada0-01228b1ff327"
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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3mYSxcO7FZ8+7KPs695a8m0QCdqTVGvTS48K2FvSUAUBgNtCNhlkoFMrAlwq+IDSsMHEEtwSqhrBc7eP373ky/uze+2CMQ9MYVKcleKkxf8Tg2vs9Dh9awpx6kFVgNuEdloHLzCAO12YFeAJZBZxYmIccjJlCTQyWE4I3QLEEygXDFwQ3AVy5frbKyLhXlUdRexRKo3AeymvAafk9PACvwN6E90IL4KVjRpGA4dAzB9q5N/7+WQDk81UFueOgV5Ylnv/85+MXfuEX8Dmf8zkAxEb3C7/wC/iKr/iK7DmTyQSTyeTMdlIkE1cAltzqOafdbVslpsex542H3dUS+87t+56VvlVPLxDm5cwLEDQT0rpddbagGVZiVDXQCwVfKDCxjI0VACb4QlZ53gCoZLWna25XfuUJwyw9zKlD+egStKxBixXQhBUnklptUSvr85sK+wV0ExAbI13AS7/ngI+57V/ueZ1pV2vAObmXnIba+Z22aWKjtLXwO59bq7soGI0993YBXtv82WfV1f6627pyKWxM53kSkbwfRDI3R+ZFa0BtTujMvD4+7gvjQFcOqvFgTdCVgtcEXxLKI4XZQ4RmXqC6p0A1ZeiasLcCimPG/GGH+btXMA8dgVa19EkrsHeAItG0AGFYWIGIAQdw6IdyHjPrYZZzTG+WqA5lLi2WDG8AOwXUVOYAb2S8mxWjWDKKYwd92oBqGzQ8B1j5z87LffYwOV2GJDef5n7Ly2Q3PiD05ute9zq86lWvwgte8AJ84id+Ir77u78bp6enePWrX32u9nKUZfrwuoDXp43lqJSxg2UICC806EZOKq3mwImmlIIge8CrdZAKM8h5oHFQtYVZKjAZsBYtUteCjUxCcwCi6ZkVQ9fyV5w46KWFXjSgRQWqasAGaiMARF+h06wGlm7L7U8BLH7OgVT3ex8I5tpApqhqDiAHpI+GA24zHb7Zic3vY0DwPAAW271NGt8QvZlbkKb7uovc2zIOI+WtFAgEqKD5BdagHY+BCoUPjEJ8B5lBlkEWIOtBpYZyBF0xylAow71PwWtANwxVMczSwdyqoI4XAnhxgem8tN8FiNg/Ilnssjw3xYxyVcHcmmN6Ywo712ANuFJBNYCrATuDAPKSMbnlUd6yKI4qqOPV5nj3vAa8MH7OjKPMIj43PuL220Xjf0BA7/M///Px0EMP4Zu+6Zvwvve9Dx/3cR+Hn/3Znz3j3LJNIjXURy2OOT9n2xvbTu7c3AC7LZNc2qYXDS2r6mfAg5lBXl5QqhqQ1tDKgpXQIsoSWNEa9MK1lGPoFUOvHHTloZcN1LIBGisDoG7A1gWA7QG6FGA26E7a3Nb93L2XHT22NiTpAwdgjs9F9id9j/bTARmj8cfj0v93VIZoy8sArNus8QE4A3B9C9U+TWHXhWw4MTYu/4NGt7G4jIASj0nYivYY70ULjIDoPVgXYK1AzoOsB2st49gytHOAFyDETUDVFtQ4UCUaFTVWxtuQVhUljBVmBjkng9qzaKjWQlcN1PEC5XwCdzBFsy80qS8IzVwBYBQLj/KxGuakFkZnVcn1m6ZljXplxPiJkv6GfVo8KQK23PKQ3JVVFo6OjnDt2jX8dfN3oTmP2+cxdPetFLvHXMrKcUh2mUCS60etKh2McQDK6jNQnOlq1BjwtATKAn5ewk0NWFMLgKzX9j3VMFTjoSoLVTvQspYBaJ3QHN6vQS+lAtPJKqUQExCkYP/ovo6k1doRJh1YfRRosn3jWSQSr9HVjs+AXs6JqIeK7tPw5fSzg3cMLXcuGQKw3P3s0lZf29uueRmaYJ+NE5vjNTtBtk2cdSDaafx2HFpaDU5rkDEytowJn5WMgfi+R8DTGmw0UBj4WQE3NSAX+kyALzW8IZAHiBnkGfq0gTqtQJUsMGFtq0XCuaC9rZ9zq3EB2YUjabW+nzgfmOCMUxYyHxCBjQKXRsZ+ZQXs6gawQmeK04pfU/8JAG+MKc9bx01uHGxzZPkl/+O4desWDg8Px/+GuEu8N/skOgm0nzMTyRgHkyFvur7jxkxWF/Yq68oIreMM4G12CMGbB+ykPfIe1FjxiQEA6wG9pkdZU7u6JceA9SDnQI2TlWykMlvKxoV78IlH2xpECCSDP2o9sa+I/QmH5rQurVvqZAPwUroy2d5HrbYA69y6H6pzrdwgTbZ1f9vc7zzkoXnbtL2h92dX0DnjxNFzfp+Xcfr9kjTJizy3Pg/unaWr1SfmhBYIC7MGPK/EazM6vCgCrINasGh4RoGNgtcK3hB8mdgOPQDHwqiE95t9wqRwAihdhqX77keNNNof2+M82JP4jFQswEa0XhhHe2RVh8P92o7neQ14yfU2AG9AcmandbduD9V5V4MekNfOusCUe5i5Cav7cHMg12djSOWyjeZjZLQXU/c47wHr1s6g1gU3Z2qPjdpe60gaBx2RrFpZgVSgS5UKK1G1CVzs0RoUmVsgpOAk0g7C+L8JK8aUxswBXu77mVvugF/f8SMdirYNwDGMwQdMzuOh2Xd+d9s21//ztD3qtP7QpCENfGfWZqh/6bkqjgux1UEBXBgBQmBjcUZVAwQPSBQabqbhC1qHBjUsoEgCShyApmVTPK+fffe97AvTAdZanjFr79IY1hQX18yi1QGizcVxb217vQ3ASxiWM88t+zjHKRGXvUC8q0Evem9ufA8ylm7qaoHbBkHfNYbsOoM2wu5kka7augMs/viJxndmQu/aulJqU3dW3Z7B3oGCpkbkZKSF79GWtx4UClxo+KJYO8QEGoa9D5rgWgMknwzMSMVEWqZLeXQGLWkdNLHkmDiI+zS+2FRuxZk8ww2tLvbBj59s+96rdF8O8G6LcT7VsHZ1Vtl27Fh685K1uXGHnl2gdv93bXx943vn3yRhBwiByvO8wZC07yURWCvwrAQXWpxVGsjMGxzJyDMwFds2eRaHsUXos/Uwx9FJLDiGxUkvHUM+cZoBzjhrtYDXpTTLQgC5NYXQmppt7AaTQ9aC4WUeYQLgAMcbGl53rIUHv3VBdDsdV7pyV4NedGSJn8fQTUO8/pDn5TYAHFolbuzLvQDpynvz4mfsCGdcfhNPqQ1tL9ryooE9AB9ptQk6JN8p2AlaYFEE8tIGq3WbrDXcXgFf6nZ7pCSJGar2YnBvAg1q/To8wgroUbQDtE6RPcCVs9ulg7nnmPQ5ZAdhbnBdQEvJeQrnJuTusbnv57h4/nOfbAPGdH8XSC+iId4hGbLvdbcPje2tEs8JYQutLUszKDAnMnYSEEkBouvA5RiqdiiOAvg5GSfkvHhKrmpxGunSmrn5IPFQjlR+S6tGsDMGPCnA0wncwSRop9GBjUCeoUI4BayHCvbEaL/nppH2PUsYxJkED+PYkPT7pY+NHrmrQS9qetmHM7C66HuQfSB2WymqbavndnB1+PFUCztzbgJ4iQGdlJLtQIipQds2w7cDVQZip10ldgeeaLipht0Xg3scIKwIXgPKMpQFilMHVXuo2onTi/UtZQnvQSvxAGPvA5grQGUomk7sXet8ksb9hc8p8J/NsrLFXteRPjvvGI/ePkeLPq/CS5MxTiO7aIJ9z2msJ+htBMchYEv/9zkR5SjOnSTYxaJHJCsFYi/UX2BFBNREsyIbwDECRKQNg3ZFzOIgVlnABe9qmziJRVozLnzTRVWf7S7V7MJcQEZoVi4L+IMpmutTNAcylr2WIPXI8MTQJLNw0PUEZBmqcVAnMnbpdCnPsS9+df2jnNk0BtBul+Z3V4NeKmde3IGJbawn5xBVNbJTZ/syNOFuZF0JE8aIH56Z1yu6rrdmBDxj1oMuDAKiNd24Edtn1m2x0S2tyYWGNwqsCXai0MxDALsjsAF8QSDLMEugmZNkcGgkgF3VHsrFmCQPvTAgo2UF6xnwNgzQ8Hxcct/d4HNFkg2DPbix7Soz67jSfX4jNbptIQhD1GWfw8R5vNTaPnfAo3ehl/4fK7uEMXSBtXvcJWR0GbvCv8xjxo5tdk6OTfrcAp/3IFLgoKG1zl3RE1IpIFCi0EoYkMCeELM4hwFrsIuemcEs0C6AOSwUoWXcdKRd7KZgF7xMo3em25+guneK+ppGM5Px6yYkrCVBYnM9QVlA1wp6FUCwYejlBOXRFOZhLfeyQmtlorD43OzQpgIytFAZ+u0vS/O760FvVyAasrMNTVTnWg3uktNvzL4OvRmBqqvtpZQmGdPm4GwHAbC277lAxaQBtMEOIStCE+x4GmwU3EzDTYXy9IUMFBBg52hXiWYJgAmsgeIUmD5KUI0Sw3yI9WNN0IWGUkpsBW2Yw/oezmRz8V4GFyuQUetnlBtoPpkkboP0vQ9DdqY+2TqQM+/CmXN2DUfY0v5WTXEb5XmBjC6XucIfQ3OOMW+k7YUdazuwIgHD1CkranxxwWZUa/OLtr041ih9d1NvydYMkIQBpYvTAITUnWbaeMCw+DVmHS4xLeH3JqjvmaC+plHvE+xM0o6xEZ+a1lzhAasAsySwkjFrZwS+puAnhHk1hV5VQNPIwjfOIfEZdO3m3WeIzXm4jw3pHntRuetBb1AGKM5tNrood8z7rms7ydjvBp1XQixQm/w2rkYTJ5QNJ5c0MBxY2yYA0e7KAr40cHsF7J7E7vkyJMadyl8EPDcNmRsMw5VAfSO2CdgTAjmCrmTVSI5hNIO8gbK+HYxC9/g25Vf2PmO/oydZ/N4nO1Ka55U+r2Egv8i6LbTNGIeTvn0XAczY3lgN7w7bA8fYiHK/36gxn7IxikKcqk1T/wqgmTAe4yuoEEKBSMAw2PsovtPOAdatbXjeJQDiNueHrskjZXdUADwjXpk8kVjc5rBEdd1geUPBlxDA04CbSgoyXZNkiPGACoqk1wSeMtxUEmO7iYY5mWB6qwBWWg50yHtGb5mHt4HdZcvjB/T6nEG2SE67S7fnVoSj+zOGysx9j9fp0yZyQddRmwsUxjqzu2k1PA4vP4B1jF1h1vaF5Jp+auDmJZp9AzeVhLh2Ii+8NwS0mh5g92Sw+JLBRp6VagTsvAHqa4TiGNArhnEy4JkgA9b55Lr6rG2gD9yS43opzZGT+TbnJmlic0Icmjz7tg2B4aXKWMBLAWvsuBnS6HYBvA+AM8xYavlCYQw+VhoI9KQxoKIQ8JuUYipQkXmRsUA2seE1ks+SrWRcQTa7UZJVpU1z1mF3Uu0uLHh5UqA5nGB1b4H6kNAcyPhtrnnAE4gB1gxfQOjMFaE8DmEXAOweoT4E7JzBhlA/bDB9r8w1TCpx2HG9C84+W/kouaR35vEDeqkM0TUZVXub5+e5bXljpavVpQbruD9tvpuaK+fMEm18SigHLou1OzIAWKwBpw1loDaw1pcGds/AzhVcKVqdKxGCZyF/GvCBFuFAi1AdtLqG4IvNF1pFnAreaRInJOVWhOLkDXDLenB2EkhvteEBWyf0bb9337vRN3i771TfwL4t4QvbKPN0IbANHHedZPpsgLl9I9rdVSsee3yft+2oEKOcBKqz9XKOOWeBTa3PM3gSQCIEoLdp/Kxod2gayagS8736zdJXLfuR2rwVtTF2ZIyAq1KyyDUCsL408PMCdq7hglmCFeAnDLcXnMSsMDfUAOxkYRsrrSgnia4BGd+sgGam4KcGulNhgog2w4KS9+5CoTyXtEh6fIGeorPxViMHF5BfiZ9LugO9G0uVkwhy3evuoO0R0XplmICDZIIIhnQtlCZBh1AFBGpUtQOFCx1SkQnIxQHCCpBUSZKFnXXQ7oBAXYb0SY7a0a4sQS8l7sisuKVMpOqQZHxpbRrbcggC+TCG7AO6fXTm5mXy9qLcvqHzLkXGTArbbHW5EIUh2RbLd4EwivYZjQTfXQEv9/1crE7so1fteJWyPgyuGwG7EErQJnaIWU9iWq+2QkHQkmIIRGdMpMka1s5nsvClohBHlbJozRMwCt4oMUNMNFiqGwEeUjVlRdDHGn7qA8gxeMKAl4oPbipjX58wiqVHeSKA2ewRdM0b8X0R6Hf5DXKLyN7f8UrT60jnpQOws01n5zCFIRDrW9l2QxFyEsGvR8PbALyY10912o9akopgFv4Trb3EQsxea0uITiyFlhyAhYAXx7HlBfhUA6iQRELVwVYXgMxrBE8wBghQC0BX68oMMfg2eomR7Ty/M3ZMn/+ce2aXKNt+/z5Hp75whTsiF5kUura5vja3aWy7OmdtO2fM/o1Dh593n+dg9/xzgV/OuYWopTupcLItZmTxkhwCwDpuNtXsusnZu9ui7T6hNHk6gT+Ywu0VcIWCn8T5APAlwU4VXEEt+JEDdAWQV2AtoUdcMrhguBlDOWrHq3KAOXWyYFUEXUlpskhnZtM472DL2wp2V5peRtKXrru995TtmVeSjZvtDQFeqrn1ZfvoanZbbHlt822ey46k54XVF2vVUhysaZ3cduM44eI5DCBfaPhSaM2o3bkS62oL9frFEQ1PPrsJwEUwiBcM+KghitrXFqXVkPp9hYAxxX6QAhSvY/ZiX7tenIib40TQoYMvQctLQU2a3B48m4sRO1eIwvk7Pbx/V1A8L4gOMR2343obTfQ/11xYSXdfjnbbya7fCTVihc0SWxnqciOZQtTsukHs7QG8HrPSIQE/Y8D7M7iDKexBCTvT4nQyCQHnOtjv9tY18qCCx7WBjNewEAUBVIfkFJDxKpodQS8JehUczRoPqizY+53G3FBoT/b3u2Tb7+ML9Lqyg3Z3YS/NoR8mttsHciMlzVzenplSnJ5DgLdDW9JdqZYSIRvcnhXAnkBKBifHQFqlwFpvaHfg8AcIXUMCnEJNAggaXjzGTRhuIitFMINPQo2uMPh0zRLATiwxf5NiE8CHwCAHeGPOu6AMgVmf99nQ5Dl2Yu7pTP+7tsu+ofRluZV1zgmlr43zAOsdcGzZplXkFjFDtv6BC8UTNoPYk6KzqYMWE63jbMO21jyR5LTcHCd+rekBwtqUBm5eoNnXsFMFO4uelnKMmwDNIbd5PYGExeF2CIOV3Lc3DFeIMxpmADFBN0oqrdiQNaaxZyjYMdK1q6b/b7fc9aB3BqzOOXhGUZlRm8h5Wuakxz7XW1g1XiqzP10Nrqswq82BED24AHBSAYGYwdZv0Jgc/igkjI7bxeYHoSFbC3wYFCRxdnHQkEcLfBGFSXJNixcni6eXWQoF6gvR+swKbSaXjdgjTlaM6QBI0jwBGcCL56aT7jm9eHOyzdsst1LtiwMd049B2ZU+7KOGuqCVHr+t7dxz3tZeH226jU69bBmwH+YWKkNOSNti+tbvchiTShaO6OaU1TqbuFnO7XHiSuJuYzA6l0ayJSWAVx/K4pSVMDBuwmJ7t4By1CKdXojjiislHq/1vgFkPmC04UqmUDDLBubWEliuAJeUM4p9jOM1MxZzjmJjbeFnFlznkLse9NoHOBSyMPYh9R0Xt6c2w5zTyQ7SBba+9FnxuDOOKx2vzTMhDMEYTo1dg5scKE4qWovGp9aGbjBCJgkkQCR/3sjgUQ2LrY/FttfmPlXC+StLcgxUa+9jhfXxWJ8DoC1R1O17+xw61ObW9GLbJuQeGbuK79Kectl+O96YOLELy1BGlbEgOeb43HW2hSAM2fFyv9ftBsGBtvpot7hts5mz9v/edyihPLlb/dRzmxawPTyO9TEOWwBicnhfqOBlLcHmbhrSihVobeh6RTJGa6wZnODU4gsAIPBCQVmx15tVGLcMFKfijGZWDvp4JWnImubs2Go100RJyIy/7sJiJw9OUhjlNZORux70WukDq4u2kWurB/DGlveJL3Xu+FyWlVHtxheskNVf2z/nQdatvTKNAmsljirBWYWNBJ2rRqhHYDMlEXkAQdNzpYBfLMgQQTFqfgYAOQU2ovXF/WwIHPJykgsUZ0hOjZjJfSM5dM9A+iCSPu3wtocojPGQHNL6ctt2oUV3AbdcW30B7edt9xJlrLZ+LlMIewng7twTu867rZIFcZhrzjiupaJDUdpSw00UXAnYAHhyDsTa4QGzoDZ0KNriya4XpG4CmBVBVeLgUpwydCXOaJNbDmbhYG5VUMdL8KqSsesTLRVYJ7RP7/uDSB4/oJdKd3Bte+h9q9VUq0tF9QBSJzly63jRTZqs9Xp7l68P26PnV1ZyTiyek6KtSbLpwrS59vzMiEdXqeAmqs2sEkFM1xy0NXE68YUkoRXgE76DDdaUSCWffSGOLnEQ6QrAKumuxdpFOnhxqiYBPGvXAz9qedtCE+Jv0v5GAxTdOQbdNkDLrezvmJfmecMAhjS7nH2u+9z6gLS7v89O16fNXaJN77zadKp15JxaLizpc9pGIaf9j5Rfh+lpUw3Gc5RaMygKrZ1dhdS0noDihFCcSNu+oNYjU9UM1YRzwjXMglGcAsWph1l6AbvTBrRsoBYrCblwkh4ty07FSSJzrx9QD2c8XkEvyq7cb9+g67P15LwoNzyraF37Lf0MbHLzG9dS/W13r7PRj/in1/n2ygI8m8Ben6E+LCTQfEJo5hJ/E8FKaA+EdGEsK72g8ZEXjl+F8CEftD9l15RJO7g01pRJ1P7ioLKAbhhm5aErhrJSgYGa4MnGfp2zL/XajIuAPhvobRos2zS4XQNs79jA3iX0oM/5ZJtDyjanlfM4tOTaPgcQ7vqMd3KdR/69uDRwzC0gcqxSNHckx3M0j4RDVc0gSyF8SMZhcSxaW9zPYfZfL0bXC1mzYhQLj+LYoTiuQVUDWjVS16+qJQwjVn6Ii9PU/JB6U2ekzwRwJ+SuBj323DopZnaOHzDpsZkfq/WY7Gp1fZpI9MYKbW3E3UQX5Ahq3Ta6oLhFNgJUY/9DKjK/P4U7mKK+XqC6ptHMJai0OUDrpkxWAspdKUClagJuMvRKNDzVcLD1BRALNGcMZRB7X7QHrIGOLFoqk8IxHO2GnoVysX6dRzOlc9Jn1SepFj7WJf6SZNeJcsz+naSPppTO9R+Tbk/DbsZqcGP7kts3RGHmNMOuZnSbftsxMX25sJU+r+8++jPXVu64rKQeoCnNaUznOYmjGTmCtpCUYl4Asc2jSesxymFhqiy3FRR4EWrp1QxdeZiTGupkJfF4Eexi9Yc4t+UyxvQ8z8iQnNtD+RLkrga9rTLWRhclBbzE0zL9L/s6WloKYGltt0xNurPX7GzL0KG9Eo9rU47FJLMaPClgD6eo7i1R76sAdoT6Gkty6CLkyXSScw8B0FQj8Tklc5s1haL2FgYUMcTTa4LWSYUV4OZCixbHYgA3i9BGfEw+DjiGXlnJRlGvg1s37QBr8N9wiY4r33QF3KdtXLItYSh7xJ2maLIyZOfral9jKMUxAJqTHMANXa+PCs21OdSfPrmk+L9tySuGAt/7NMReGrW7iOlqfOm8QeJ5yaGKg2oQPEUBO5O0gWaxHqsq2OJlISssTCwDpioH3XioyoEqB7WqQasaWFVS7cFa6VsEvL45qsfLOfecMgf277uE3/KuBr2daIWuN9+QpICXoy83jk3oyOB63MbcxAkxp7F0J+74IkT35U77GzbADQqU1i+CCv3VAnrNQYHqUKM+DPE6oRqCn3lIzSwG5g4wHqQYbqXhVxpkxfNSVywDJJod7RrAohu0nXGr8bkpw809fKmga4XyFtrYPmWD51ewDaijJWgZisjGEirdEiu5OnnpwqQ7eMbaTXYAw77g2b6A2l3B71xgeRHHle45fWEGOSDqHtunbfa1MaYvY+5t10nvkrWGrv2vb1/u2L5kBmfmsi7A9811iU3PpQklAMmNW6z/Yvo/cSRbNyHaobAvyjKo9lArK5TmspKKD95L/G/yG2+kHQvgt+HE0hln6X2ee9xcwm95V4Me0PPCbD9J/qeUYJTUU+rMakptTMAb2c1Du+SxCWg5J5j4Epvk3CTnXnv9FAAT7XIDRON1Wq2HWu9MN1MhkwrWpUMmHmyE1oQS6pIrDbXXQE0duPBoqICyGuUtauP1WltBcHJhRWidWGaSWkyoE0k2rWoBPHIAlDjImKWHXlipvLxYifdXoEs2AC+xEQDI0yXps+1zprhE6Qs2j3Jee97G9j5tZht4DXlljtXm0vOGjtkVuHaZpIb6O/Z+dpShLC3rS59fa8nZgYcSGfRK1rbnE5qaW0YmanmAaH2xdJCkHJPFp8TbBgDy3Gp95KQ6upgegtkhrZpAam17jxdLnO+GnkMf4HWf0+2Wux70tnpa5bzT+trKaWSR3kyyH7S0ZWwrZjTxam1jjBXA03612lhwNkmcW9iG4yxtJl52m9rOJs3aP1jYyKqvOSA0+xDNzkD6RyweKZZAlQII8IVGOa8xmzRYTEtUmAHQweYniaI50pNyBeg6PgcGl0BxU6F8r8LkEUZ5KlkbYpCrWXqUj66gby4kvqduWsDbkI7rc6+WHP+ngyWdFPvAbwQYdgdoKkMDN547ZltW+sBiyGbWt/+8ziRd7W+sptbX121aY3rOeUH/AjLWkzCXMituz7XXB4q5IOwsCG6zaaY277A4VLWHWSlhXSbiqKZrhrqJ1qYnHtoc4m0Rzot2+2Cfi9ljuqEHUZKFPmkZ4hvjuMeJpW8cjFl49D6Hc8hdD3rAyJVSbgD1hR4AiMHfLVWZxsYkWhW0Pmu89bzW3NIXIK1mXBTrdpwHxYoIJvwkSSHVjT7m7jXl/YHw0gpQeQPYfYY33HpXklWikS2lLT9lkPGYlBYH0wpP2T/Bu/R1rLAHchrkCGYZwhlcaLcWTrOYESjwKZPHCJPHGOUJozhxbaojXTmY4xrq5qlQmnUjz6ebSb6j7WUD7oMmfia2KZULanl971NuwtqaN/B2yhhKMt2fOy/d1wW3MZPMtsVkzobY57AyRpsd4zRzDtn2+w15GubCWMZogDmKb1B8zIbkW49mCqYBWjXQlYNeKbhC5irVoA1BIrd2WImL0bWTGrexs62TGbAej8yJRpn8b38Pn7AzfGb89TkAdZ/f1ud2SQufxwXoDUrfoOw4qqy3x0Ea7GMd6hNKND6KBVuDxkZpYthYKsR70WYShwuKlcJT4EyuS5EmiBXEU5tfV2tM7y9+dk4Ar3JQDcMsgdoB7roHCg94gjrVUCsJQ2AKnpVWwTPhKfNjfOj8JvaKCn+onoKFPRAbnw2BqiGWz2sGeXFYsTOAiVCeyApS1x668tArB3IeatmAFtU6g0NwdT6TdWWbxAHj/OazSJXFITvVJcuuKca2Smbi3tnTbdvEcBn2wK72N8YTNAWsnINL3/axWuYlOKpc5JxdFj2DtrycdO4tzeMJ56Rae1VDLRroiYaah4wqwW4XY3DbfJsqpAAEEFOMRTpUvKuF2qTuwvJMNQW/3r7DPY+R3uf5RHdkAXZ4cdKBlzu+A3atVpV6Rhq9Dvg2GjwpwYVua1Yp6wHroWoLWlYS05JqJYoQy/e0IMYswOkTasGYjWBtAEJL5miDVAOlQLM2FmpVY3KzQbNHsHsKzXUC7Tn4SoMJEpOzItgZQ9UEf2pQTQqUyuET9h/AJ+w/gHccfAj+u/poLFfXQ+we2vJARcUoFoB4bhJAgK7WfTanDfStpbg6ey/VoAPgre8tA0SZpNIbmnQX/D3LM8jRnKnkaLv4ub3gMDDmHBFSF+wLa3uZwcxx5TxWOxtqexs4ddvOaWQ57TJ3fg6IxzqqDG3P0aR97Q/ZOs8pfTk5x/zufbasnPbTO6dF8FAQJzHnJB5vuYIyGoUCWE1BBwYxqYTXtAa8UCezDS2CUJjkGWR9yA26novALON1I9vK+vug3f0Cws5d+m8X5a4HvUHAG6A0e+13XcCL9reikIoAZQE/KcATDT/RsLN1dfHyWNL0kC2hlwXUrQVoVYO0aGBt3btUy4vZRzSt41/afoZy5OkLFe2Hub4DbfFKNBbmpEFxWsAsALVQcKoAWUJxQjCnBF0DyhIaDzSa4JzCTDd4bvlePFnXmFKDP3/KDfzvWzMseSI5N6FglowCHsWRk5pajdTYokoKY7JSUFUDOlmuK0FHOtMng+VMHs086Gx6s/b83l3nobF0244DKRez1fbzdtGbY4BizH3sqjVtcxDqttdnuxtz3W2eot1rDvV76Nhoi9qyQBljs+1zShnltNQ5PhcOk5x4pv8A1g4kjQWRAo5PoZ3HpHIwpxP4qZaMSopChiWFNscuC42pnNj4qK2aIAkjyK6zJSGEKGykG+smjcg52lxEdlkc7Sh3PeiN0vS6lNFAJpU2fVe0v5WFxL3Np/DzEm5WwE01/ETBFwQ7kYzmrgTAGt7IqqoEQKtSNLcABmIDzFw3JIwlVi0QknOSyidy5t1zNh5CZzA5B7IOtGxQ3rIorikUJwRi0fJUsz5fVXK7dk5wjcKDqwO8o7oPTzO38D57DYVyKKYW9aEBQwNMKG8BrJVQmSsrmVUWlcTd8XpwtM4qQxpUNx1b53fauoLso3+BYepvbPhKR+4IyEnj4+jFXTSwoWvFNtLPfdpl7pwcsKU2vO6+vvuJMua8bfsG5LKTCQyFJoQG+zV55OexbXNbS3MqJRQnAAagbEg0Py3ApQFrgjcKds+ADYXkEeJprpyXung2/G/C4rSxa1bG+fwYTu14cbGZHHfp4+NK0xPJxrd0f6DcixMn28SmlgJerEQMrcDzKez1OeyegS8V7FzBG8lm7jVADJilUA5uokIQaAGyXpw5lVoDQrRHxRdFawG8lD9PHWS8R+sSmr5EG5MT1lqLV9KhxoJWFYojA7MqoFehkGQJ2CkDSGgODxSnBD81+JOH78X/nD4XH3PwF7hl5zhuJigKh7pg+AmjPgTIxQTVgGoKyaPJLPE80WaX9tevq0T0VU/o/X0TgO+rQHHmN26BMNHut3mEdVfTmUG+bRBfqjPLmME9RBfuQgPmACbndLIrtZnrR/e4Icp1Gy26rS9j5Zzn7+KMQVqPBto+T9GNvob5owU+QOaNGm1VIGqs1MosDGhSSErBQgEuoTO90JnkHKiybWUW2GCOiE4sG7a80JWu897tlEv04L3rQS8rAw9nIwavC3iFaQEHxrTJmu09c9T3lLAzBVeQuAOHwE/JYgK0bsJOqAM3JTTXSrAm6KWGWigJ9FQ4k3mEPNZUZ4crj16dYvtLaVF1Npt522R4iRsLdVph+vAEdlpi8TSCLxl2xvBTRnFLQVdoq59TQ7CNxp8c39u29Vg1BxEDOhSGVVK2RNeh4gIBsL71ItsIHFckK8bYr5zB+xzVE9JyS2fbG0Gz5Fat3Qk4d90tdNYd897sgtAQYOS+50CuD+B21Y77AK577e714qJtW9/7nF4uIpc0mQ7JeWjQnP04fMkCH1vJLk2QsUaNEe/zsoBqLNRCgYv1lE+RpvS8BruYAN7aDQ1vw8M6zbGZ0fK6tu5LkR1s79vk8Ql6qSbVJymdCawdQrQGFYHSLAv4vQmawxLVoVCXboKQa5LahK0x8bIJZThYURv4CTatG7DyLBqfDy+QZ6wjSpOXCFiDchqLEyspA20QO0fq0yeThw82QutAywqT958CiuCLQijZGcNfa1Arg9n7NMgCpOUetPFYWYN33noyaqdxtJiirgqgVtCnCnoltkBykjzanFqoRSV5+eLKMPkN8gVfO9tyade6VaKxXrAMUp7bbH7AuEm7Q9MMpZH6gMuQfW8bFZqekzs/d52xtGXcl+tPn6PRGO3xDsjtzg85ZN/rbkv/n7l+BEjwuuar96DCyDhxUjyaQsFp6ppXgDYInZtm7YnZzZKULMazNS2T33MoRd+FxswVvZlI90H0PNQNgGsznJCcrwgwZgPweFbCHpSormnUB0LpgSTDgS/QekD5EkJzeqEQAYB1yFPpNUAENTMoNEEdC/XQrrDOAEDoT2Lra+18PnkBY9B8zJhAMRee3Bc7v640crzEBMCBOoArCrgpod4j8IFFvVSYWglfIAd4T3Be4ZGb+2iWAnZUKRRLQnGLYBaAWTKmj3nMHmpgjlaSXSW158UBkDqtxH53ZaPye5zYqF1RnqkOD6zpnESy1Gd6mt8E4zOfgSzgyeZN55X2mpcFeJdF3XSBJAdOfc47Pffeuy09p3utLsANUbBdG+KYvt0B2nNrOMJtAN9tMYCZneu+BI1vvRiGAB5ZsM2ESMU5D9gEulaz85v7sp7W3Ptu5OjeC4Ff9/3yzfZzeuTuB73uQNmm5aV5K+P50WlFyWcuDLgs4OYFmn2D1T0EuydUniRxhWhFS7QldBQLENqp0J2FB7whNHPAlRrKKvhCYUIEdaqAuhEPqVSbA4SDN3q9nRmslIRFaBIe3nqJBfR+3Yb3a+cXAGAPZgI5B6w8FDOmAK7zPsqTAqfHJRYf4uD2PexSQ68AVROah2d4aFEASw1zpGEWAegWEqdXLDzMilE+VqN4dBEAr9708gLyyWhzHrMJgJ85Ni3LlEpaoikeDpwJd9hq88vJwCC+LYAHbKfvxjqrdKnD7vZuG7l2cmCUu3ZOg+wel6Myc33r3kO3zbh9G4XblSEzxzni6rbJeT1Ct7UZ+9Dr1BLZHWANftaKHRHYGCvZjE7dMIq4kAayIURDMqTBXooT2AYdf74m7n7Qy0mOiko8NjeyrKRZUspiXYV4XqC+XmJ1j0Z9TWhNKMAbhjeS6QCewA6IGculTQSHkeBLxdQeK56dE5TMUIDUwIohC0C4bglfSv/IeTARWBPcVIt9sPYgKxqirty6zlUtHqKEbkFWiXchrqCYMassJo/OMHt4iltHBVZPkVp6eglMPMEsNAAN1QSgO2FMb0mwuTl1MCc1aCVOMrSswFW14bhyJnVYN4l0tgCuz28f2tf1+uw4xqTVMXaKIcpRb+hZaWeO2RavR4ryMUjp9aP0AUVOulpVV6vLAV/O2aXbh27722xqfVRmF/hyx+fuY8w959rZcs7toKVHe4R2+rprmEPPgfJ/w3krVnuJF6JNnNgeU75uPvUfULQRkL7tnT9vDGPngCt6E8DZVeiYOJE0G0rky2PRVaUk6LzQsPslmj2FZg+wc0nWzAptSR6rGWqmoCuCXoUEy3H+VaGiwVzoULB4d0IBymqopoRhBhkl+e4AcKHhZgK0bqYktCAUcnUlwRfUelpGG2Jx6lGcFJLi65hAqEMHGnmh2Qcw8GArn6luoBcrzE9mKE73sXioXLerRDtVjqFXDLN0UmrktJH4nVUl4RfWgq0LhnN/xna34bCSK8mEZMUZ6dtuwHn6e8X/3UnOc7ufOXz2fn1O0DYHbYG5644BGZwdqGMcWtjz+QbuLudsoxXjMWOv0weYfcd36dRc37pgPHRsru3utbdpwUP3t4Nsy805spGNr7sA8NYQrQT8GMHRJU1in0rXbj5Q0qw1F3g+oxnmYgx3XVSMsp9eEqV8d4NeVzK05saPvfFZIaYUa0MTgnuvnxWwc41mruCmJMUYJwJ2XHhAM6j08FqDQzYW8pD6dJ5ab8hY3UCqigtSqVpBVwbUeKhCgyoHaIKbFWgOjBR73aM2Kzo5SMHXEKcegVWUSAVW8hMaACqs6ogDyWkt0hI9sFa0S+9BRx7lqkbx0ERWgJpAjsEmaMONE0eYxsogCCDHMYCe/Wb+yzhQog0u/Z7sP+M4pAJnHD8D6+/pBNcG60cHoXBeOGadBHydmJud2wTaPiN8eu3udTNyW+KPgGENaxfpA4qxq+Uhu1q6fwikUoq020bu/G3XHNo29phL0BQurJFdwrUH7Xzrg+V/tPEHANxgPpL8vhv2wK5pABhlv7tdjl1jwj12kccX6A1JmGSJgqOIVmhzaEb7UWHgpwZuZkTL2yfUByFh88wBpQdphjLy4zsvj88dOKj9BqZwsI1GszRAQ9ALJR6eJOqeJG4G7JSg5wZYujb7i92TzC7NnFBfI6FQLaDqoIGVaDns4lRyakYa1WuCmxoAM6gjAT3yHqz05ovqGWApmczOSQHXkwVIK6F8vV9Tvy4clzqnREN3qtl16ceM80kb/wisHXVSSY3spLDOQqM3FzKpHRZePFR50ybaOvxwUF1jn5KUZutDOR/Dt0XT60tFdW4Zut7YSXoI6PrayVGP57lmn/PKEJgP9aUrY2yaY465JE3hg0GGEp/H7WeqNUTtryOtBoc1OG6EJPRIer2dy7sNSed366OFzyuPD9Drs+G1+9X6v1pTaWkwOmuZqLnQsDPdVhyOGh5KDz1xbdYDd1pAnwoNyVMPUzgc7q3gGTgtJ2hqA1cYwBHgldSvAwSgAl0Jr6C0eGi6KaGZC9A2e6I1kifopXTdTQEKpjOzIjCx2A0ZcDPRWLlQovHditnS/Zq3j0DgGYDbABNusPkydbMrDDmndIPLcxRmbJPU5m8VtVBKgFBRWJ0GoFLUauSbWnta9SLpu3MhV6AHtfWQaJ3EO3sPOUeaPBDdllXtkOfjtvNiX9PvcVtO20r3p/+77eW0tD77W/cexvR3jCPOSJr5zD3dLhmiW0dq6WPfm2006hiN84xGmHuWpNbAFvubtj0wBtLPl+7YNYZtOKfc/aA3RGd2ZO2xGTUKaqskRIrPGwVfiqYl6XoIrFjmZ09Q2sPVGrTQoIaAQn7spjI41SVu7C9wfbbC0WqKxaxAVRVwKCFR6YBZikOLnQpASWiDFHT0RjQ6u89gzVA12owvvgBAovk1eyQ0JUmGFW80dK1gKmmncAwd4nPkZfRAusJrQwnWGhtR+pnWANcFta5jSgdAskAXP6vEntqhoil6q8brJcH3be1BpTY9W9OQjui4YpOMEjHnaQjf6N7TqLi/jAyVSUn379Dgxn85Pz+Bt23nJt8ctdgFjm0TypCzy5AWlmunD4z7juvu3wb82/q2TTMcS/em7Y3RoHPXby877r3YlUbtS1adA6UzGV42GxrVv9su234TUnhiem92V//ZYxI1mRlE0Q0+OaYFPQ0EzYuVMGeqBsyJhgXASsNPHNAoKB/ASAMoPJTxMMZhv6zwIfNbeKyc4dHVHh7TM5x4gicD1qJBmiXgaoKyFEIdFHzw+vQGgAfcvocvCeRIbImGAeOBRqF6EqAX4kSjasmlp1eE8pjBykDVpVR6cE7CGByk0Y60Ni/vJSwi0IDc2s14gxrckPQYYJO+BNZaXWLDowScoJLfpmt3jaCUbgslmbgwEkdplNgflWjbfmLgJgpm6aCPV5ILtLFA3YAhMT3k3Po+EzBvwS/SPDnKcwe5qLcaRy07AwyjJs0xGlSn3Q1ask/GOMj0nRM/p9v6gKdr6xtzn9u0sLHaw5jJ9oNAxtT/26l6Q8/5t9te94GQuxr0NvBuSw7HVpyD5LJ0YE8S55ZkOmEKKcZKqTysLGAWBFVpqIbA2oBVAM1gUlKFx2xWY28i3pNLV8AHe5IiBimWSZ6kkKsvJEG1qQieAFcQqkOF+jA4sPjQ9p6TBLKFgzEeT77nGE+enWJpCzy2muHWyQyr4xJwBLXQcI8qgBVUU0CvSuhltab0FG24KHMah9MFtxTkhrSgSE22oSBqE+xIbVaI73pppiDYBVGfbFchdnImCb/9RPKV+lIADwpwpUJ1TcGVwORoiv0/W8I8chJ/JnCIKTkDfPEZREk9e/2mlnSZsXpbz+3T5PqAYpt9K6eZ5EBw2/Hb+pn2tW/bNmp1m11vm6Z6WfToJdmQLq2dIF3g2sWLOOcEkwvEv2MVRPrkkp9ZKnc16AHYnLhylRO6xwHyQH3groNnIhEFzUiKpOqK4Q3BQ2LYNJEEcDcCiHYGNPsMBOoTAKrG4BG3h0cWe3CeYL3CalnCrcS2p5dK8nQGUPMm2ORKgguVGtxEAJU8QU0cJtMaSjGIGB928Bg+9vAv0LDGu1b34E+n9+Jd+jrqqoADYFeEZp9QrTSK4xLqpJD4vfZ5KLTI153wOzk/h2x17fEp4KUhCGmhXRM1s4SWVOvzODz3DfEMMIOsa22tfn+C+p4p3EzLwiGpCya0dLhFJ8+zvlGCeA/6MVrbN0MKpg3gi3GSge4cS3UOlRi6LTIEGH3H50AsJ2O9KbvnbOvrGOeYoT52+5WjRuP33CKgj3od6k/32ttkjLPOJU/e3XftIgHv3e19gLlrGaULy23UqO9u0EvpsbSYajohd13lI3WlAXgHtpDJmbmNmUNqBiO0MWzkpYgqawErVgCX0n5VGfiCoFR0+iBYq+C9AhyBAg1JLsTfhfOB4NQSL12gdTpkBkrjsDep4ZiwsCXeW1/DXNW4tzjF8XSKx2YznABYLjV8wXATgisANxWnHNIK6GTsyTqkdKULeCkYdijltv5gsJOuq8Mr8KQA702l2G6wmwIAa7Wu4OxDDUBAMs7UVuyc0bmICG5eSkjHoQLroGE7oFjwOr7QMswKUI20VV8rURBJTKT3SXYKfRb4knvZCMRNJthc6Zf0/22bBMZOvl17WN+5Oa2qzyml79px/xA1OcamlruHIYeaPpq226+U0hwju9jqUvkgoTt3kb5Yv4vG1p3r/b+NGl2f3N2gpzU2vPgGZB27FRwYorZHXia+qPFZD2VF24uAJwmmQ31XFo3MF4Db85jcs4J3Cs2ygHca1w5PUWiPRV2gLL3ksnRF673JBpKfk8QeyEroVJCAnS8Ybs8DisErjdWkwMG0wn5ZQZHHn57ci8NyiadOjvH06S08NN3H8WIKIPRpCripAClHSrHzHJIvmw+pzzMzLcOUZj5BB/BUWHykCbsPprD7JdxUw00EmJgC0EPu2ax8m6DbLJxUpagE+MAMLo3ETe4RmgPRsgHJJEPhd3KlaMcIWXB8QUBBYFNCNTMo70FLrOMXodeZZHKSerD2DMpRKaK2SQ6cdpkIuhN836S9bVLvo0JzINa17Y1xZunr39CxfXRrzubXBbtum+nxuWc95vmP2d73eVsbY691SXLGbjwgY8HsXAu+3HO/zfd+V4MeaQXKDYbUxb5D02Vzz8WVvfdQtYWqCyirYBaQSXYutKMvhEaze4zmmsfkaQsczFe4eTQHGgU2HloxSu3QaIVVXcDWBuTE4UTXkr/TlQRVy+Qc7XdeS3iELxmYOJBmsCM4q1A7jRtTi6m2MMrhyeUJFDEa1vAs5YBgFaAYvoj9JIljU1IbMGZP2aDw0pCBThoveY6JdhefU5fSTAEvJOzmaSllmUoDu1/CzjXsTKHZk/NipQq9Cho0q1aLdhNCUSiYpYKqZBHC0eFTC+DV18U2WhxFi520qRqgWAi76gvVBvOTm2FaW6khhjg4HUgrAcGMY8s6hq9nssV6kJ83C0Xb7phtOUkdQ8acl9OQxkw6XRDqs5mNoVS32RBz/e07doi+7JOuJrnL5Dqk9Y7tQ2f/BqCMoYO7x11EQhtjnGLuiOyiZV/Ay/SuBj1oA6jkFjihLgEQS9YQ0h1tsBMgHetRkfNi07O82WRIPea9aFP20MPcu8KH3riJxmncBEClRzGxKLXDvKjReIWTxRR8amCOFcySoCuIIqIlNIEixUmyTbwHSSob7FnM9ioY7aGV/MCWFa6bJe4tTlGxwaP1HpxXcE1yL0oARbRGDWU0qF47lmQT7qV0Zx/t2dLDyQuZAp7SUni3MPDX5vBFiB0MVHBMnxbTs/kihGGE56Es4KuYsxQAjMQcAvBGhdCEROuee7iSYGfUPkfViIanK9H6yHO4roZezVF4iMbnHDhMHEQ87NgCZAfYubW72+EcMUYD2TahbKNH02sNOdEkn88sBvq0uqF7yAHcWFvhebXnywKcbcexx0ZoyuOYUr10IYUnZMgCTScgKteu9ml6nVBmZyT7ubbthDZUsIMpK+EAUBGsGDxzuHHtFPdOT/EXx9ehtAxI7xSsVzDKo7YazmqopUJxGquMo82bKXa8MCnHgrQe0BXBGQWtPeaTJqS6Izy03AMBODAVHmn28GB1iL84uY6HjvfAlVxH1ZLcuk14rah13gCwOXHEib6b9LrPkSV97mlmlBbwJKMNzybwpQYXYotjAtiI04muxTmILMAThCrsAv4SmkegAuAQhgDW0CEWz83kJVcNwJoxeapo2YuqxOJoCl4JuLuZgjkR+6lyFKrZE5QtQG6GglkWNwCYm1bb7o6frLaXPL8+d/CdvTKHZIgey9muugAxBIRDk/eQQ0ufdpdpKy4MRmszQ5pnKmMBs0/OSznGcy+6cMlRqmOOv6QF066emY+ncAXgLgc9nk7Aqmy988it7U/sfQC/xDYTpfvieA5qFwNOspnommFWQmfqCnAz0S64ZKhStKWTZgLrlXhXGg9bGRwtpmicwq3jOdxxgWIlmodehT4HJwzJy0mtA4dqAL2UQHg3g9CaVmMewyDqAsyEW80Uihg36xmOqxJNowFP0EuCWYbJPuT69EXnPlNNrwtouVyZqaPQkIYXEnZzYeAnRTgf8DPVelkC4sCja8llavcIdi73rldSvsiVACbhuTgF3awHmg8xjMqJ5v2hN27i/7v+btxs5njk3j08tNzDg49cg9WlJOdupEo8ABQnUtdQ2RKqstCNDXUI/TpbvFKbmeOzsYlnJ+BL1faGNKjcttznbVRn33W3HdcFoNz3zHkbk2XOKaXvmt3rj9CaemnP9PxdNcDcfZ0XbLeB3TZt95I0vHM5rOx4rxei/G+z3N2gtzcFUyG/R+uI4iRxqnXBXiP2m42Yq95JR2hOtXIwCwU7FbUsBqpDAdAMpRmeCZUzIZMJga0CVwpLnmB5cwpaaZhToTVVBUkhFi9JCHX4uC1JpCzABlJ41hF8o+G8QtUYlMZBK4bzwPsXB1Ak11/VBVyjgWi7SvwyIqieyVeJoMUk99xKSvN1QxTC9jMJvKPzSkjWDS3p0OxMcom2zjom/C9CqEZ4Dr5k0fSaQFOGiAdvADsJ2l0tmWaktBMQjXyHZoV7i1M8fXoLj8738P8AvJevwekC0IzioIIxHstHZiBXQK8UitOJLGqsA5omVGaww96sI4LVd6I7dwGk/gtunjPkcDJGMxvSfrrtDIHL0OSYu+Y2p5QhkBrrqJK7drpvqM1dwPaDWC6a/mzXe/xgBLsodzXo1fdMASpBNlBWlYOqLdBYkLIyQTsJS2iNSn1ZMEJcGOoGemngJxq6YSjHUA3BrETbsYUUZ101BlqVqK1GXRlwrUBWAbUClGRMMae0oeHFYPZYeJaJ4E1whvBAtH2JxqexUFPU0wbXD5YBXBUePt7DzeUUBGDx6BzmMYPJEUHXoi26SbjN4MyxvkG/ca+90lfXLgU8UpuFdwsDnpTg0sBNDerDAvW+LBpakIu0bqByfaz+o4KGt88gKyWaIt3rSoJuSJJ2I8QzTuTzwyd7eM/BNfx/+38B7TyO7BTPuvYwGMCD6hBaezz52gkYgG006oWGWSisjkPGmuUEVNUhK01QvYGzmeeBUdlZLjXhbpSL2I9yNrohMBpDpabbx/Yht2+Ms8uQ5DSmXB/Tvm8Dzz6qOHf8UN+HZNvxtxE8L5L+LAuEA+9T9/gPNnr0rga96rqBK0romkGWoSsPvbTQi1pyNGoFWtVSascBYHc2IwgzoNfpzKixwLKG2iuhGpaA9BmgKsB48aRwVuHEEexct83QxIEdCfBB4vDanJlAO+GTF60OHLQ7prYEESDamjklqFqjcYTmOnCkGCr81VUhmkujUD6mMHmMYJYh7i84jSiLNrawG7IgmVmSF7DrwJFqe33OK/GWNuLxjFSav1Zg8SSN+jq1AMzRsaaUfgLBIWXK8KUHaQIbaitKEIu3K2tun1lM0Sb7gaPjGf5feR+WrsDTp0e4tzjFRFms9gssqhJKeRjlsbJGnnHBIfhfCvL6qYE2WhYpiRaclVz4Qo9zy6VKTrtK9/XJtgl6jH1om1Y21FaOttxmG8xppUPMzDaqtO9euguBdFtX08wdvyu1uY1O7Wvzg0CDHKQoB/p20eD5UbJtYTQgdzfoXVOwpYJZSVydWcnkyUZBrywUEWDFqSVEcCUDKdjwUlBwTr47B31ao5hq2KmCPwbIyyROTnhOxwWWlQaVDqSikT46Wgj4uEkAv2mgGw1AjTirgMU+1RaFhRyjGwE+VoCuFJplieq6hjpoUEwsXKWhjzWKIwmpMEspM0QeaOYMGAJZRrGUIrAtbdc3sec0u8y2NjwhitbrChWx8O7coD6QSvOrGwxfQoCr5FBRPnn5NYMKDwrxiKiUVD0yUt3CTQA+BUAhEbcO2mIQvzB4WAnVCwAfc/BuPLW4hcobvH92gOOqxGldhkgUEhtjCTRzcSLypYGOBYVVp85YnxPPOSah21F0NAu+fYDSd9w2UOy7XvecoYVAX5tDtsAhQOi7bnps3yJhDO25bWExlvbsc9YZsxgZ0/4dlA8mDW1DSOEJ6b3Z7BF8oNBUA3itQu5McZU33kM1xTrbBxCSL8cv4SX0DEbI5h9pzkWFwiiwJuhGwy4JzZzQ7BHIE6xTaA4BdiSFZW1IM1bTugp5CfiQLJpLqacHS3CWxflkJR6GYLQ/IDGgbbBjaXFO0SuDulao5ga0FFthcQqYUxYvU17TpuJlKqWHpAaSDwAfBvmGY8GWtyYuCmI1cs+bledDJhwui7YIbr1PqK8BzXUP3rcopla8aIlRTpqQiYzhnGwjAk6rOaghkA2OOJU4D4lGHLVmlgTd0W6pxJb66PEeamugifHcvfdhX1eYmgaPnM6Fhl4V8McFdBVSwMXwEE3gSQFa0ObYCYse+dhJS7ZldbkLrbMT4A0BwzZbVE57yXfo7LWGKM2hvsTj+4AqBzhDgDlmVT9kBxw6Jyd91xtrLzyvfXDoWhc59pxa4wcD4N0OavSuBj07B1AgpLMSkGFFMIU4PZAtQNYjLSNDgdLktEgpgFg4MeaBJADqCCiZoeoJzEShWChUlUZTibZGTkkGlIkSsFoRVCUaiQSzM/zcgWaSMJoB+FrDhWTKbqVAjUzGHEoZ4ViFODPArBi+RpvBxDVmXWQ82PCggPow3FustB4qNrRp1XKemn2Al8thesZzM4BeouWBErudYeDA4sa9xyiNAzNBK497pksY8qi9xqIp0XiF2hospxbqMQOzFC9Ocyr3riy3wftggKygu6oJngnlpIExDouqwO+8/z6Yp3rsmUrabjTqZQH1UIlyFVVphAw41FZogNayMFBuTfvGyhM7lBzqS8l0KYN2iPIaC2S70JldQOujHnNA07W3dTW6sVTo0P31XTPXft+2vna6zyI9bgwt2Xf8GBn6fYcAett17iBletkgdTuA964GvRis3BbILkIuRwSX95WW+K3CSGLjSHd6qTUHAIyeRMNWCsaqY6BoHExp4EsNVZVQVks8nAplfZaiZqlG4tK8kYwhsfjsZNbg+v4CALCoSjRWw1qFBiVYSxYSEINqBV+Eid1KDklyBKMZdiY2LrETMtyUAlgSnGQhg1lKomywgIZe1JJwOlK5cQBlck5u2PIAbLXnEa21yLjNMXQFlEeE5sTgqJzjYH+J/UmN/bLCjckprNfwdgLPBE2M0ljoQjxtzRIojiV/JkXqN14+Kpc+LA4YmJYNnrIv2WluVVO8d3kIAHj0dI7qsSnUUodwiHWAvC+BZk+hPJbSRGS0VJAf9b71a0td+8dtK67Zt62PLoyfczawofa29aHP9pa2nQPL7jl9fRyyC6ZaZA5Mh4DyPNtz4L+NFh1ru+ueN9RGTnYBsTtAmV6Yyr+E87bJXQ160amBDQRsSoSUX2HCCSt6LjRIKcm8YTRoVbeTepzGCQgUnhd6K2gAVDcCko0D1RolAKgS3hjoKRInC9E23TT0x7A0apXk3/QK86LBZL7EyhpUjcGx05Jg2hO41u09gdHG2wGhMOwCAIknoy8Y9SGHJMvYsAuSE8Azpx5q2QBOstKEKPcdHm7H3rnhALQJjC197CUWz5wSpg9qNCcz3DyY4OZBg3La4OTaBIoYjy5mQjs6LTkEjgsUDqBwLzHRtzcCrhTn8Qh44QdbrEqspgWetneEw3IFz4R3n1xDXUlVC1URdBUy4cTzrdgL3USBS4OYIJttyM4CDIcvdB/T7Uo2PTThDe3LgWBO69hG7W0Dzb5r50BoGwAO2fjGAulQf3NUb18bqVbcp72OoRKHQGubhtjtzxhNbYjavQMa3pAMjouBvmXPG3qPRspdDXqR9lI12rRWQjfKPrHtqZB42YuXphWakaw7O7l1c3WGAGZKsu6rpYWZaJQTBdYquOSLV6Avw3VLoTZZMxC8LgG02VoAybJiCgvv5Af3huGcuHpS8PyMn4slg28CYMk9SZZaCtMjaHgNoFeM8oRRHjlMHqtAy0qSKseBrCTh9vmfd3cwBQ3S+xBzKHGHumYJCLcEV2m4E4VmUuJdj00BzZIn1APEUn1icrymhe0sVK33LPZOnzj8aEisIwHwgG0MHlvM4JlwYyqa9Kox8EztceTWCwnlxPFHBy9RDrlJo5bfdWQ5U3Eh+0juUGWFXSeui5y/DSTGUKU5SrVvwhrS6sZqPN32+mjLVEvcRdPNgWh6rb6+d88Z0iDHaH+5+0z33QHAu9R3fkvfzlxrYyF1vkve1aAn+SWDRkRrLYANwU4JqiG4iYaqQ6yeJvCEQLUGarsOZo/ZOaLTxsZFgtoBCAA2DqpyMAsNrwE7UzKBKsAVgJ0y/ITBUwcykqnFOTl/r6hxVE9wspqgrjXYS8JjrT0UPBzW6c8AATxlBUymlkJiZmpTlkXqzywZxSmjPPEwC4/ysQrq1kKqhsf7A9DWD8x5bG4tNSQemGeqnJMAE5ihag/dKNG2K8l3zVrAjVcEPiXxaAVa1VTXgKpEY2WCaL4GYJKUanrFMm7jPBV+Y5o5HB4sQMR4bDHDaV1gr2xQWyP1C8MlWIdxH4AzJrbWKy/2XqS3QztrelEuJfn0kAxNfutO9O/fBfCGtKRt2kh3+1j7Vg5oUxnS7LoAO3QPY7SEsZrdrtrGZQPQmLYu4XoXBbmxHsy5467ozY5wqCpAHLz+Is0X51Ut1bWbw1Lqs4UJSVcaqjag2gKrRjK4tGmowksSJ77otRhQhpil/FDtoYKWFqlVP2G4GYP3LPTEYT6v4JyCtbq15bngUehWplXn2BD80qC4qVHeIqkHlwCeqhnK+ZAeTSVJmSX2b3LkMX3UQi8s9HEFWomWx3UDeNd6I25UneAeAOzG6AFnwxViFpaYb3Mq9s42uTSjDTtovVIdAJAUqQ+X1BVab1f5vbAu5aQRPG2pXQjEQHZvGKZw2J/UWFkDazW8JzROS1q2ZJy4idhF2yQBtAbQNNdq9v3aoaDsbbHh9ckQbXYB2ifbXvdzjv7b1kZX00o/D2mCYyjWbRRhbvs2gN2Ffhxqf6zcKerxkkC37x3vA7exdr6cM9jQ9c4rdzXoAYCuqXXb17X8pyT42s4VOPmhyQOq0dC1h14aaKOglg045mP0nSoEzrXAQOTAhREqjzk4rYS4Lw3YuWh4JgDetdkKK2uwqErUtcayKcFOgS1JJQVPEs5wbFCcKhRHIU9nhbbQrApZYcgyypsWeqXb0kG6EvA1Cwt9FMCuagBrRcOLgOc5ZB5JqNsIat0E0xuOK0MUVqCOCw17UKLZlyK6LlaVPwCaPQYX3AINK0BZcciJGmAKaLFQr7QfQG4SauXxWrP3JYOdwiMn8xC07zEpLJgJZWnhZhrWFvBT0d71itYLIRMywACtZ28MPM/G6e2QfuxSYvJ2kZzmch46MCc5m1a6L9eHLh3ZpxF2HVH67iHdNhYs+/Z3j+3eS5+22vdMcv3qa6f7bHJtX6ZssyVmZMjb+CLZXC5y7O1aQN7VoKdXBEMhIFyhtSdFhwg7oRaQ0hRgqpFAdlMqFJpgiKCW1JYYgsfZqgNRK3BCi5Hl1oFEgrDlD6XHZNpgUljxUFQehXao2cBbBVS6jUkDAGpCrb1VSCVWs3huOognarBNasfQKwu9sm3Vcb2yUmV81YjDjXUCeD5MGN2XJr0XoDO5+7PA10OFUqQ15QvAQDNXaPYkgL8+JDQHDHvogNLLQ3cEaF6Hx1fRQAeY47Ci0wJK6W8FL9/jNh80N780WAEgxShLC6M9psZCK49HAJx6gtca7IyA5xTACm04iGoYtArPbIx21HNMmn6sz6nlUgbvkCYx9P08ADikseQALueE0vdMtzmY5L53z+v73qfhDd1LXx+HNM5tbQwtEu6Aze087eWYijH040UXeulxdypd2V0NesqGHNAVr7UEXqfhqvcorOyjl2E4kUUDLE4ZgJEVv2Mo70PMFm/adeLkH7PyWwE+XXuxs4XrxfixSdFgXjQotBOnCoinIQBJKN0ER4s4plyIu6vRhioItQkJiwhqCjkWTc5DwDlUlWjtkrmq6J7XtGa8l/S+zjzUzICJVRVi2rEk0TSHAH7WAnZ2BtTXGPa6RXFYYTptYK0GEWNSWGjFqK3GYjGBLY04GXnVlm9yk+D16pPfC6KdsQ77GAKiAJTyUIrhPEERY6ItJsbBThvwxGKlGbYuoRoKDjaMyS0HcyLhHFJcl9f2zq5TS5TuhJxIqu2x59szcIc0ibRfQ84Ru0ywfRpLX99y+9LtQ7a6iwBS7vpDdGWuvaHrjaE6t1GbQ8/yTlGb55AxGt9Fky9c+uJwhNzVoCd2PA6pv4A2YXMjIKgcYINjhC9EU4ju62CCnQB6RtArLYDSOAlp8EGFixNftH8RSfki56BWFqoyKE65rRnnpgAqqZg+LWwbm3aznqNqDJRirACw0oCRuDy1UFChwkAMLo8apJ1JLTliuTdtFPQS6zAKz6L1lYUAknWgugE1Vl4gn5Rd6Gp5qUQNL6U6Q8gCEa2dWACQUm0ZIZ5KJpbqusHqHtVSmva6Q3FY4WBvBUXAwbTCflGj0EId31zNpPitFQ9YTt7C6G0ZK1uw4fY/eQrPiOAZmM5qHMzkB60ag0dO5yi0w6ouUNcGs1mN6X6N1VKjfMygOAUmtxwmj1bQt5ZAY/OhHLnFwAht8Nwr3jF2qG0egen3MW0P9SW2laMUh+x5QyA5BIBjbHW5PvUdu81hJXcvuesMUcW7glVO++s64NxF0mU4xrznl6XJkaJsLeyxsvPTfvvb347P/uzPxn333Qciwlvf+taN/cyMb/qmb8LTn/50zGYz3H///XjnO9+5ccyjjz6KV77ylTg8PMT169fxJV/yJTg5Odm9842AmHjjAcWCYVYcPCmF1vQlwU6B+oBh5yGOTgkI2j2CnSq4qW7j+WD0piNHChLOCR3WWKjawpxalMcOk2OGXgJmQdCnCotbMzx2MsdpU0ITY25q3Lu3wI2DU8wOKhT3rFDs1+DSBw0Ror0osRHaqdjsoo2smRHqQw03k9I9AEIQPoDCwN7YQ/W0AzRPOYA/nId7oI1nNWifSyf5Pk0vOq8o1ZYR4tLA7hlU1xSqe8Sm6UsGzSxm0wZP3jvFs+95GE+eneLJsxM8eXqCqW5QWYNJ0WAyayQHZwR7ixBXF4Ce1sAHBaGFnSQAoJnDwazCrGjQOIVlJUB3fDLD8uYU7rjAyWNzrI4mMMcaxSlQnHqYhYM6qUFVLeEcXujNDc0urSgxsnpC13uzu31QhrSMbRpBF5i6E2nfRDvUl/TcHFh1/2+7/lBfctfuAljuet3zut/7FgljACynNY7VervH5PqxrT+3SXorgYy5l+xpPBrw4vGj+nObZecnfnp6io/92I/Fm970puz+7/zO78T3fu/34s1vfjN+7dd+DXt7e3jJS16C1WrVHvPKV74S73jHO/C2t70N//2//3e8/e1vx2te85qdO18spNirrhjFwksMVsWtB6DdE7rNl3KnvuAwMcfvAoJ2rmDnknmfSwMOFRoAnAW/UHkbjYVeNjALB13Ltc0pYE4VaKlhG43GaTxS7WFlC0y0xbXJCh9yzy0840k38ZR7jqH3mjaInfwaiN0EoZafaLB2BjSzkOYrap0sWp7fm6A5LGD3NOrrJew9M/B00k7a6/I4HVozB3RdRxeVTly0tuUpWSBIGSFJMG33GXbPw889zMTiYFrhQ+a38KTyFKW2UORhyEMRBzunx2xSg2YOMSJENbIAIJtovU5ynVIjTj/eMPxMruGZcGs5xelygqY2qE9L2KMS+pZB+ZDB9M9KzP+oxP6fE/bf4zB9pEHx6AJqsQIaKzGLzmVjF3fNubk+9JIomm3aUB+d2NdO33FDgLINHPuu2QW0nONKn6RAkQPvvrbT9of2p9u3UZq5fuWOGdKwt93r7ZbkGr3v5gjgzQFUSumTosFj8l0bB4LnWkgOyM705md8xmfgMz7jM7L7mBnf/d3fjW/4hm/AS1/6UgDAf/yP/xFPfepT8da3vhWveMUr8Hu/93v42Z/9WfzGb/wGXvCCFwAAvu/7vg9/62/9LXzXd30X7rvvvnPdiK6l4rkULA32oema9gSE5nQaACRlmKolqLzeA8jrFuCM9aLRxdJDROCYvzNUYiAioGqgVwZmoTAxEkcHEFgp1GWJh9Q+TqsS07LBflnj6fNbePr0CFPV4KH6AMerCU7UFNFT0RvA7QG0RyElF9plSZt1hWit5RHBl9JvyQjDknS7MJvFXtc/kPzvVhHoOrOk23XLa8rzMBo8KcCTAvX1AosnKVT3MOyMwRMPmnhMJhaKGA+upAqCAuO+2S08qTjBu+gevFtdw/FqgkI7KchruPXkJAcoA6lqMZU+e5LfLmrDbBjOKjx2aw92aYBGQa0UzEri/WIl9slNhlkyymOP8mYNc3MFWlVAVUs4h7VrajNNyzbScxNYD/ghORetM0Qd9tmIhoAobWeIOuzTwPrkovaqIdqzC3Rj6Mah+9q1b7n2c3I72txRep1ORl6jz6Ekfh7r4NIFwrGOL/Gc3HGXXZvvUnXrBx54AO973/tw//33t9uuXbuGF77whfjVX/1VAMCv/uqv4vr16y3gAcD9998PpRR+7dd+bafrsQ5ZVxS1VcgjIHD09ks8K30hgeOShgpo9uXP7hHqfYVmT8HNDXzU9oA18AHgNL6tkeB2tbLQSwdz6lGcepTHjPKIUDxs0Dw8w61bc5xWJZZNgYI8bphTPL24iZmq4ZnWaczKtTt+jHFzMTsJBwq3keD4dS5Nhqrle3R8UbVb59tU0SY3oOVF6dKaqfNLekzQ9NzcYHWPRnMgmihPvGis2mNWihPPaVPitCmxcgZLV2Cuatw3uYX9osZqWeLW0R7cUosm57GmqBcc8oiipTkBrNOQecA3GnZpQKcaxU2N6YMK00cJk0cJk5tAeUuy00xuOgG84LhCjRXnlS6tGX/XDQ14O/0yZhDuNEh31ehy2sSQVtWn0XXPGdIwh2jG3Ll9FGef9GlnfZLra5dqzWmAuWv27dtV7rCdbpd3LGplfYu1nHNJes553vduzN22heKQueCitOilOrK8733vAwA89alP3dj+1Kc+td33vve9D095ylM2O2EMbty40R7TlaqqUFVV+/3o6AhAqKwd38c4d8WHGzQnbwKdCYS4LYBJoWbAzRggxuRhHfJYEsxCwcSK45HKSzQCCY0IlRisE22v0CEQnmBWjPIIAKQQbG0Jp8RoGoM/NTdQeYPDYoW/WFzH4nQCDpSrqsWepSoKLvUSoK1dBAMPfVJLcuQQW0awUEdLTAC4uQEcizZT1WvqLqXpOuEIzLzWCLtA2M27GasrkJRtslONZg9wM4CLqEHK/72yxtPnR1jYArWXtGC3mhn+vLqBuapxUK7gnALfLIWx1RwysMhzgEeI6UNbbsgXQdUloTzZA1RplI9plEdAecSt84tykpaNCSEdmfSZgESzyziw9FVX6Gg/3ZXnzjLGcaV77RwlmR6To/9yWk6fBpnTVsZSczkqNgdEOY0ttz93rZxG2N2/S3/66NPctYY0zLStbc96F9nl/MyxQ9pRbt9QwPn2y/driN22dg1j2MVjdKzcFd6b3/7t345v+ZZvObOdFZDWRdWVA6DX2gGttQNfRkqQwaWHMwR1vUZRWqzMDORDaZuVQnkkmUZEm7NnJsc2ns86UNVI2IQmmJB0mrwSECaSSt23SqyswrvcdbwL14UlNA7lxMI9xcFWBv6xEr6Uig16JYH2eiWeqZMjj8kjNdSilsrusSqAVyDnoesGqjDi0bmqwU3IxJK64adxeLtKAhBsNLg08KUSR5uJVD+XMA+Jn3v0dI6JtjgsV7DMmOoGtdP4vZtPAwCcNmWoMh+C87VouWRDUD4RHGTRAsh2vZQKFGxYgPDEQC8I5S2gOOF2kRDfaK8BKiRxgJtpqEaDp4VowWfuz7fPZ20D5d4VfhrI2902Ss5Dg+Wov75Jdlfgy+3P2a9ybfZpRUPaUZ9tLHe9bn/7gKzv/vva3nbtIYq1ey/bbHd9sqtdsU+2HDsWALv7hwAvp4F1Kc0hLa0PHO+Ec8ulgt7TniaT2oMPPoinP/3p7fYHH3wQH/dxH9ce8/73v3/jPGstHn300fb8rrz+9a/H6173uvb70dERnvGMZwjoKawrE1gGaQ9WkrUEQLv6h6c2UTN0KOzKkveS9ix8KdqeNxRSmQUKEdgcQD4OgrC/sYAiqGUDE5xfRMtUArxMsAsNN1ews6INTaj3PIrrK0ynDSoC6qkBGwJ5BbOUgG+zZBQLxuSxBua4kuoQNqQUCxO1lEuyYqtiXseduY5Pb4aqpCEA7PP2DKEbrgxelBZQtYI3Dig84Ainiwne5a6jNA7TssHUWNxczLCsCijFsFaqGkCzxOM50WzTuLyYqky0XgIa0fx8COpXNWBOE61exxPD720gBX4plHuaaFCjW89MBkIyaY8ztrwoncm+m31lfdglBdXmwK1vXw4MunRizvki3dYHEn2A13fcEMikch7ATSW979zz6NMqu1rgWC07d/0x24a23wHZxW7W3da3vfu+D10ztyjMAV7umkP7L8tJ7FJB78M//MPxtKc9Db/wC7/QgtzR0RF+7dd+Df/oH/0jAMCLXvQi3Lx5E7/1W7+F5z//+QCAX/zFX4T3Hi984Quz7U4mE0wmkzPb2yTEDLHpeW7zVZKFTIAGcHtCa7ZaoZKT/EmB00ZJlhRPkufSSs06CUT3Ai4A1lXExZGFHUAkqb2okRyeNDHQiuALhfLYQ9cSuG5mBHcqWp9UZWD4CaE5KdEsSsAS1FJJWaBjQnmLxcV+yShvWZhbFWhRCeBFyjJoImmKtVa7Y6E/2SZxeqmM0fhieaUoLOBASQ095YSS9RPAT8WISobBjrA8naA2XkooKcZyIdod1xrqVEMxxHNVA7QMJZJCjKLX0b4JeM3r39iJQslKgDA6vrgphTJTjDbvJoumyBpCefv1789pbcEdZNsq9VJklwl6iLbrfh5qa6gPuesNgU3axhhtMwdOfRrkNoq3b8HQd285erPb9jbwugiNeQnAeN78lF3gGus5Ga/V57AyBHy5c/uu0QXBM8deIE5vZ9A7OTnBH/3RH7XfH3jgAfzf//t/cePGDfylv/SX8E//6T/Ft33bt+E5z3kOPvzDPxzf+I3fiPvuuw+f8zmfAwD4yI/8SPzNv/k38WVf9mV485vfjKZp8BVf8RV4xStesbPnppsAzhNqD5hl8N5rPMojBztVqK9FrU3+yJHYnzxAlUZxrKAaI3RidJzwABtahywA8nK6gKKAgCY7cCMvPAGguoFamBaA3UQLrapVm/qMbPBInAnIYSnX0LXQmqoRB4zJLYk3NAvJHKIWlQCrD7Xx2omMQ7/iA0kSS+eqpec+d6Vr91MKxF7UJ+cAlkTdZumhKxVCCsKjsQRMPG7ccyqPLahuy1pKJvHStGnXpKICAw4w4d45pIuLIgnF4/NGm2MVCmDNos3HeVQD5EMuT4+wgFkfL45OEmMZ85F2C+luUJtAdjK/EK25qwxN8pudymtLQw4ZOWA5z8Sfs911v+9qi0v7tw2oc9fPaYNDfc+dN1b6js3d22XZ+7B+74ZsXl3g6dvezbuZYzO61+5+HhuukBs7fRphrs8b7ey+bgVwDtD7zd/8Tfz1v/7X2++RdnzVq16FH/zBH8TXfu3X4vT0FK95zWtw8+ZNvPjFL8bP/uzPYjqdtuf88A//ML7iK74Cf+Nv/A0opfB3/+7fxfd+7/fu3HlfAI0RD86q0VA1oziW2LlioaErLRMyQSZbSwJSDihvKpS31nYzVoCpGLqSvJptVXDPOJOE2rM4djAD8DKJNgS1WAXaMUzyCjAlBaqOUHgO6bBCdYHQrFlCNMKVlAgqFh668jDHDdRCPA7Rrf+X1nlzbnQQ9UWEmUHWgRqH4tSiPNaSb3MG6IWCm3nofY8PObiFjzh8EAd6hT9ePBnvvPlk1LVBU5fQKyV049yDNUOfKFCz1tpjOaAIfqzlWDCAU9VmbGkOGXYvPM8FtdXWAYRAdgRgZqjKQ1U25Nq0osF3HXzO3Gx+RN2WmLw+GUP3pf/T89Jj+mjIIcDI9aN7bO7csZJzFOn2a5d2ttGM2zTFsYuKsZKjp/v2nUOG3r0hsOrTrrYB3S79GttGLs3ZEOBtgPcFurkz6H36p3/6YMkVIsK3fuu34lu/9Vt7j7lx4wbe8pa37HrpsxKorpg5308IvFJQdQgfuKXQHIpXJbzUeFMNoTgmlDfFpV3XaOP7VMOhrE9i4wlUYWsjiz9KtO0pCI0Yis2S91DWhQz+peSlpDVNWDoOXqXU2huLEFRvVh566aEamaRVZSVzSNeN3ivIMieduAO9kMbixT4PaHbRg/OMJ2eaq5NI+EXPYj+sGphbFSZTDa8NyCmsWMIrGMCz9x/C37n2W3hWscLv7+/hP+tPwv88fTasF1tlfF/JSvYVs1qDnlRMp800ZBMHeEIsnsEzh/KghtYeq9MS/uESqlEwTXCEAWAWjOLEo1hIySV1GsotNc36uSQa8Rktb0Byq+SdJEdPptujbKPeuufkaMi+z33XzF0jRzuOmbhz97atv7l7Sq891NddAH3ouXf7NUZDGwuMl6DlAWcBLNXUhux55wW1vnNzzMcubQy12Rf/d1G5K7w3+8TNGGQCdegBDhn/qXEwp4TJsUZ9JM4pqpGAZRAkc8pKHCPMwkspovQ97Ma2sQ9gk2hXKry8Pp7rwdaCYgJo7yXIPfxYupYLxMTYqvZgI44WeuWgVw7KShweWdEyqQkB8rSmW4Wt9QJ8GiFBtvTtDK2ZxBjmJAW8DelUW2DmNkyDlXhA0skSE00gNwWrEnZOsHOCbxQeqvfxfneAJ+slSjgYclAqeHmy5Bo1xypoYxScYaLGTVK2SVFLY0Iz9MyB54A2Dk++foJ7ZwssbYEHzT5OTooQlC6UJjFQnq4Bj5bNWlNOFzRdwAPWv3PfM0tsF+deFScT7wZojrEf9R3XN6H3AVWfQ0ofWHT7P0ar6wOz3Ll92mQOpNN9uXvI9bVL+Q5pXn0APNZWOCRj+ow8ndfndbntnRwbGzfc7fE2v7HHjQHA3DFPiJCFPnEzyfZBIcibWOrcUeOhfYPJTQ1vDPRKQTmGrmLsnjCQzhF0QdDxpVFBw1C0Trrc1QDSSTEOiAh8zsmLB0g1BOdhAKiqgA+xfJI8mtdVuxWtQc5GSlXupQUdKqTQLcR+1wKfQ6L5OQGw1EmlLwNLkCzgAdljW+CrARCBKoI6XqFUCm6mUR8aNPuAW2n86dG9+J/lc/FHk6fhT1f34lff9+E4uTkDRU3PAcUxbSiquhZ62ZUAKxUSCDC4ZJiJw3RWg0ie7X5Ro1QWSxRoGgMKVLFugPLEQ688iqMG+rSBqiXEg1Y1uKpC2rGotXeey8gYovaZXAT4kja2HDBMNw45ncTv3X1DTiZDkgPQPu1qjF0x973vHvuAstu/7vWHaMbctYaO29bPXWXgvD7qb1tmlNw5lxoG0Edzx33b7MajLpHv74YG6M4PfHc16LEB1IKgl0JrkQWo8e1EN1lZmJMJ5jMDVyr4ktDMlZTAmRKYGMoRAAW98pLxxHKol5d4SaarKufXlQeAsxofAex9iOMD6HQJVTdQSkkiaASNVEHiwtLPBbXaGSdUY6wAwdFTlAhkQyWFSHPmNJQBTa8LdhsUZ1p1IQUGrQH2QhGyAJDSCpNHNWZzBTdVsHsa73vsAL9r7sM7zVPw7qNDPPrgIczDhTiZKICspICT5xcSh9ehSnxDAKHNngPN4FCeqWkMmIH3HB1C0QEaq7F6dIrJsYCesozixKE4qqGPKwE764C6EcCzId9mztGnfTD57dsM7u1zvEzHlj66M3dc9/8YG1xuUtrVftjX3yEnlBzNmf7PgVfa3yHQ3ma7G9P3Mf0d8zwG5DLek13CADI7zz7DvuPkAumFz9Pd7dfqyNCi8o46snwwiaoJxYnU09M1pBr6ogGFhMLiUbmCmRTwexO4WQFlDXxhQJalUKsLDg8rJym8PEMtQuYTazfi3SJQbNjBPDaBz1tAK4lNVwrwSrQ2rcHeC/CFagUcM7kYJVUelEz4YssKdKb1kqmkcsHGF8oHVQSK2WKYW21PHow645mYgle8j1x+zjbwfnOj/HPiRdKmegNAJ4AhwrxQ8EUJXyicXi/x/pN9OCYcP7yH4uECxZFodlLlguFLksw1LPGIZhnsqUCwrRL0UsF5gq8UTo6LtjsVo624UJ4SylskMY2n4vyjj6v2HeBmnWOTnT8DdmOC0YH1JLXNlrdzyrHzUmZ9NF0OKPu0szGA1weeYybNHFCMmfS6trTcPfSB3JAG2qcV94Fd37m7aMe5dkmNfk/6EiHkjutpYH3dVFqGqHNcf0eG2x56htu29V5yO/15HrmrQc8cE8pjxuSYURw7mJNG4tlicdBGJnaqG3EuqUuQm4AswxcCRuSA4lYNvWzavJZdh4ccBbjWiqKbYToBiHbI8CH1lZIAM6XAWoMnOuT3VPClaEhuouBjTBlJijWv0dYH1FXQYo4b6BCUTdS0gdawVtAyVftTUIt0aTLp9zkkndH4gDXdG8MY4rMBQKeE4lGF2USh3i9w6giruhAt7H0FzCLUMWSIP4wJ1KaXBcvkWLxVwYHmXACTm+J05Mv4PLjNqkNeNEW9ksTc5RHL3y0LfSoJpbGqZJERFi4t4MV7SB13RsrQBDREOw00GB74wAS6zZ6WoxKHKMO+foydjLbRg93POWDdpokNUWjd7TlKbaiNbc86B3IXoT+7MvK8sZlRRl+n+/tED/Qx7+nQQqp7re5z30aDfwDkrga98lbw0jt2KI8amFsh72TU0CJN6DzIWlBjhUKrXVudQFUWatm0dh/YtXbQlpzpqTfXanrtPgoviBFbnVKi1RktRVcLA54a+Imk8Wr2DexUKFc7A9hQa2/0BkLf1uLsYU5ZvBo1oSSSmq4hWJwiTUqqjY3jHjsesNbwchP/YD7OcE14L2DnI7gDSilMCo3Zocbp+wyqeo7yMYXpI0FzsyGf6ELKJrERSrM8ZZhT0WYBYSzKUwFW1UgVDNaANhLcz4YBllRtEfAmt7wE8R9LED+t6gsDXl9gbe64Mdt2knTCGKLUulRfTnKTUB8w5ejD7r6ha+VAN9fWNu1siD7s0o1Dz2Ab3Xq7J98h2+JGN84ukobo850lBbeYcCL1UdgqSZKKCJbptrS9dUfPXjvdfgG54yELH0xSnDLKEw+zEuovVj6AcyHOLtFqvA7u9jX0bAo1m4CJxEbGLNn3VxXQ1IEK6wn5TzUfZIAjzWIS7Hixyrifl7DzAm6mUB1qNHOZyOtrIfuIYanybgAohqoJWlP4gaOzTog9ZIZ23HqKir1NAOHMC9iXZgv9WmyUMwCY0/gsAcsV9KPAfqEBmmPxJA1ihqoZppJge3Jyb6YUj9ryREJLzNKCmkC9FgqqUVC1liw2JcFOxMHIl1JNnhhQleQkLU88ypsWxc2VAF7VrCnNxp6topDc0xmvzeQn3+Z6nR5z3sS6yQnD+3N2rLHSpRW3aYN9/cuBSx84bmuje/0+rSG3P72n9PghoE7vvw9Eh64zBJxjaM4t+y8cYjAErPH8lJ2gGDuMzW0Dnt5n2kul6+Q3RLUqCuXZVP9zj8dG6ewntekEt6vc3aC3YOiVh6ocqLbrsjExzdTGyxToOGuBpgEtVyAtQAiECbCpz9p9ci9CDGlI81OmPzQpkDECeGUBnpVweyWagwL1NYPqkNDsEexctDlJ2gwpKzT1ksPSE/hEI9bai3UCWQOABlkDVTtwbSRgnKjV9qC80JxR24sOLWnsHXom/7GSAl9j5R1khnk/4cB6TB+ZoDnQcKWCWfpgQxVtVTlJlG1OHcyphVpJJXowgwsNbZTQv6XYOe1Mw01VSE8m2VyUBcoTh+LIwtxaCuAtK/l9o4Y3AtBbZ6URMkbzG9o+WobsYDmtJgcoQ8fl2snReV1wOS/o9vWz7/ghMO2jR9NJtI+KywFWKrnnntu/bduYfdnDNxdR3VRfW05ef85piHHxGuaEDfNFmBeos6Afu0jeuF6un9F+GPddgrZ3EbmrQc8sPXQdsm3Ewq/OrdNMJRKpSiICV8FRJY1vA86CXfclSPflgI8UoDTI6DXgzSew+yXq6yVW92isbhCafQG7qPlIBXcPLoPRiwEQwx9Y+KkUSHUlYGK84VJskr7UUl2hbkTbIoXo0tQCWvehnafKQvf8TqFZVkro4RAOoOsG6nSOYm8CNzWAppCOTSokKMtQjYdeCrVMMc1aYktUoXo9Fxp6XkoCby3A7g1BN16Czhc1aBkKw1bVWsP3ye+X0JqbP2NnhbqDnDv/5lgHiJwGkgOgdH+fA0jfNXPgktueA44+QOy2NXRO9/htVGz3Wn1tbQOvbr/Gbs9JDoTPOalfmB2ITlaJ1pal8SMrRLRmppiBotgcM915L4AlM4O03mDDti6gt3mSRslp2/GYS6Kh73LQc1B1cF2NZYCcX9vioqTOG/F//NG76ag2tLdAYTonWuE20QqkFVAW4PkUfn+K5rBEdd2g2VOorlMoWhvc8T0JuBVoi7BCC19d7tWYT2vUVmN5MoFdGCin4aYhcXVJYC1enzAaZHWgOVVQajOxaBcFvPQ5pc8oVJNn79tE3ahq6OMSam8Gd88cMNxWUaCFh1rZQEfWwfEoGUBateBHRoNWjVgQYj7U+Ls1kh0GTbPW8NIg/c5iJkfhjKV1+pLs7hxnN5bKzH3PaWB9mlQfSPXZ5tLjhujEbZNQnyaWOy620weAfYDcp9Fu27dNsz2PjNEGs6flY+5G2/HSPneoy/R/O5/F7RvmFzr7u2q1qQgAnZAlXmdUygBqui0F3jOmBGCTCk0TNOTe6TO/3fm1xbsa9PTCQtcetAoVsZtm/XC2UZSpdJMxd9X+nFYXwhBaiQNOiw3PT0s0hyVWNwyqawrNviSbdlOGPfDguQMcgUKmFhQMPXEoSotp2eD6fIlSOXgQHi1nuFXM0TQTqFrDLsT5RVcGqimga7FlUgiOl1IEifYSgWmXenrhhexmbTnzone1YedEUXVOMtQ4B+0cdGHAhZaMM1XTLlLYOrTVIdpHmzxnq6UGXlydphKdVFI6s5tEutc2u76/XYPSc/t6wa/PRrUNoIY7k6c+0/a7n/u0xpHOFqOuMfb482zvaq05QBwC3KFneh473ZAM0MDdxVJfqq3RdGYG8DaALp3DIsjp8N5EZzvmtaLAfg1sMYwn1eK6C96B+STOG/2Znzrgt0tmogtofXc16JnjSoKcoxNK3WxOfKkmt4OWw86tgW6LtC9T/AMAreHnBZp9jfpAqj3YKcBF+DMM0lLPjxUDpUcxr3HjcIEbswWePjuCB2HpChzVU8wKi2Ze4fhAwy4V7B7BLgm1VSBfgionIRkhIw2zD4DsJD1b91mMAT91dsWW+97n6cnxeqcOWFWAVlDGgJ2X/qWDrA8wWACNwwBtQzAiULULnMTxqIfKjMdlv48cQN3UT+fOB9hH26X/h/o0FhRz5+QozzG2vW77Q33Jnde3Yh/7ve+etwFWjlrN3f8u2vg2GTh/KNPK1qTPA+22NroE+HJAR1q1ccIoDHhaCshVzdosFK8fS5k5D3hZVCMFr64i0JVoO0z6eGZcxudxWQkdRshdDXp0fAq2tPbUC2EKrUQAzPwwOc0l9cTc8MpUSoAw0xYzgyATcvviaXHAaOYB8OaAnUuFcCaIdudJqEwFUOFRFA6ldnjm/qMolcW+rvBQvY/HVnMY5dusJG7uUYVszHYmhW/JTVEyy8trnaQpa5qwmvMCfHKzZ0IudpWcI0hvwuqg9UUtk6t6s7GM4ZxC1pfNBNr+jIa5seocs7BJB9UOq8S+NE9xtd5NjJtpYBjkup+70tXq0m27aIRpf/r2dY/p9n9o29AxfVpb+r173W4bfZpqd1vffQ19vyyqc8v5Q+9JL705YLuL7zRF21wX6BStfQyUErNL629g4OYluFSAY+iVlThlx4AO4FuL+YCCNzxX9Rr8PIstP5WUJYtMWNec0I0T7t5vLt1jH2V9TrmrQY+XK8CrRDXvmfxyiYVjGz1azMaEHjW/rsRJOn0xlWg4HKqweyOUpi+lugKXHjAsgAdATy209piWDQ4nKyxdgcoZnNoJju0EHgTPBGYCKQYXDF8w6msSwM5aSRJozyjsOtk1lGh6baaWjG1vcOW1zf04eVY5+iKb1aUbLJ88x402m7OU5JlfLvNb994L0Es5jUkv1ue1OcqJpavBxb6M1VC6MgQgfTawMecMyRhtbhftM9e/HCilgDmGnhwDWNuOuY3n57S6dPvGezYE8vEQrdbanQpaHCnAJHHCxrQanZ+XbQ5gX2rxhFYkVWZMyBzluM24pAot6RODxkdaicNYjH1OmZbg+BIBmLvmlJRlAjacYjYkBbucvS96gl5AMby7Qa+uwWw2JsCcDaq126Qv1ZYJvb3G2NgVUnJtz/JCNBJwTS7khWwIAMNNGTRxIGIUE4uicCi0w435EoY8Hlrto3IGngXsGqdxWpVwToGtvDDeAFQCYIa1hPqAUF0voE8LUBVCGCLNGTCvTUbdsXH1Pp94X7ljM9u6z/uMJt0FwdRumtKvgULJ2mH7EmFnFyTjNLuuHWXIoSAel553LslpL2Ptat3JcAz12LUfjqUUc8Ccbu8CX9/nvr5072kIGPuk++w+gKC2rZ1RQehD7af2u+hPkAKeMYDW63CpQgDP70/hpkayPpUKktuW4AqJ/dX1uqqJzFU+VIFh+NJIVqvQHikFihmv6ib0ywe2S7W1PUP6jE1tMOMZSsn2DW9qYHiOvoA2fleDHppmU3sA8jQY0At4fd573Um7nVxTilMlQAcHIHg3WcnjaVZS2UGH8jmYA75SYCaw8bCaUZYWWjEWTYHKGpxUJWproJVvNbym0WgqAxBAMwsuFOxESfV1ApQlNHOCPSil6GxMquw8wK5DFdDGaquVbqxNJmPDhtOHWj+XIdnqAJMDw67BvNNejoLO9qNPQ8lIrnRLX4qx7v7RMqThbNOUumDQnehzMgSQQwDVBZ8caG4Dvl3thOm+7ue+87qUb19bu8gY4Mye1gG0zPm5BdOZ96l7f0kGlTNjSOs1takVqCiEwpxOwBNxHHOzAna/CJmNBOikZiXQ7AnoFaeS91Y5QFceHqJJkfMgTQDLPCcBslPxFl/VomyltnVmQBnZFjFZbi70V+U9Qztxgq3ionDWyWWnTDJ5uatBjxngXfTc+ACTlUQ3M8HZawy032oqXlRuhWAM1lCVlRI3pwq+IFgCsCRQoyQQvVBwjrAEUBuHSanhmbBalvAuoTOtAjcKVDoUswblRILpF8cTeDZolNSna/YJdq5RTA241iCnxYMy3jfc2b7n7q8v48KYWLaB55l1fkFGUxuZGzR7TNrPLSDSRzWlk1dfirEL19GThvITa59GNKStdc/v06LS9nNaWO5zTnMc+zntd+4euzKkZQ6dl/a1Dyh3oUDPAZZ9VQ66JYH6mARKwwdiP9t5hTY8qVv7XVe7C6FSbq8U+nKi4SYKdqbgSslu5EtASpcBCDjWzAjaEHTNkHqXDEBB1UJ/wqg2py9rGb8UimZLcg+1ybC1Xp/xXU00t4ICGxbrcyYUaAJ+Z5SV7vxzAceXuxr0eiU3SXc1ve62zPlnHDQysrGPPdgCpKzElq18SBQt2libWaUAvCE0K4KtFOzMoy4dwAReaZAlsGawYlCjpLyOIRSlxbX5EqvGYIGJaH5xAaQJvqB1wLp1MiBc3d5r69AC9HtwdhcG3e1DMvIZ52yAvW7NuViePuP3DtJXLWGbo0F6zMaxu2oHYzWlbbazIfAb24cx/RurGeYm7iHKta+vfZpd7jnnzu+zj17ESWVH6RZ33Yka71KjaTiCSrwwtRYNz2jwfAp7OIXdk9y+MteEVH4zMYOwRiiezShOGSDATsS73E0I2CMoJ5VPAMn7q6rgvGKUOOIxgydFyI4opoj4ub23tIRXd5Eax7QT73JonQ03ysb3tefu8EN05O4GPc8y6/fZonadLDvndR011uf59uVrpS1MymBLoKpGcVRhMlFQVoFPJBOJD7k0XSFenc2+hp1p2HmgLz0AhgSsK0jlcAPwhEAELOsCVWPASUHWeHwzI5iDAqoOIQzeg5xeV2FQXl6Wjs1MnHX8GS1447l4zi8ixoJS1xkko61lga9Lu6ZtAmcnxb64vPTaO4Yb7OSwMka22aFyIJLb1kcz9mmE2+jNtK2xoDl0je62XQFnCOCH6NNu/y5qx7sE6WMWBvuTmGAAbGp4WgPGgKYTye27P5NUh/sCeDFdn5QuCw51M5lT1InMG+TDfDMBmgOCK2UeIQ9JFG8VyGvAMVQYw6wIXhFIEyjE3bYAZzR4UoCJ2pqmYF6bW4CgHTrxujemTShB7AMrJXNrtPUNzgfnlLsb9KLk3Fy7E3Fu0spN8N12g8a3PifRkGIKLhdAMdIRTmLT9CPHmK0sJvNCvKM8g7UKL6WUE2r2FKpDgt0LGqCGpCjzkqklFCcCO4K1CoBB04TYBwbISgJm1uEFLxTcrJAXFeEl2xJQmnUdTp/R0ITffYbxGfQ9z7TNzP+N+nZ9/biEFz/KGEAboq52liFac5tG0mf/Ow+lOJaezPWtD6THAGU8vtvPnFaXXqvvuCEZC2Z3UPsbreWNAbyyBE9KSXV4MIHdM3BTASYmiO1Oh7lBCZgxQWhNhdYUEZPcswZ8IbUsmWhdQUYTuNCS+tDIwliFgtuqsgJqAPy0QLNvwJqgVx5m0UhB7toKpRoLARQe1Gj5HEHNORDsGviis0tfGNIT1aYHIK/lpVpJTjsAhifnTtu95XaSOMA2Xi9sZ2tBq0qAZ1WhdSAxWl6gqYGaSv0gDoPOzgh+HgFMmiKWFzdWEI+lgyLgqQbiJAOESgRi2wNL0VXlQxV4H/n3gQkj1cj6nmtO6xuS3KKjuy3aALq/Rzq5+WSy6/vdRk6GXXfxsZJqh6POy026ObDIgVnfvi7V2N/Z/rbT/dukD9DGaJB9/ekDo9y53W1j7HRDi4oRQHieBU3fOTu3lSzu2rhfAGk4ABUFUIRUh4czuLlBMzfwE4KdRLsYh0Vw0PiULJKV1IGGnUt5L0BKfSmL4MkptSqLRWBfFMFOJcyBVSjvpQCzUtJeo6GsXMuVsogXClWjOFUgy1LOzTGo8dBVyMi0UhJeRSQ2PEC0Qs9rO1/GD+BcyfE7cveDXp9m190/hi4a0AbPBGGnxtc2KJOFQgxOLezF2EutYdfLNu8lc5zntmisLzS8kWKrrCCOoIVUYODSIxrvvCd4rwBLICsvcnurWmL3grUZ5BnkSknmHKpJdGWDPuijf1NA2mbHGuup1z1n2yS9TRsZkFzezPh5rJxLsxvyKMxpLl071xhHl9y1cs+qb396/tDvldO8+s7JXSu9n75r9D2T7nW61+5+zkl67fR6meM5lr4ZIztQo6Mozq4pwHugKNYOLMYI4O3P4K7N0OwXcFMNNxHbXQQlZQnkOABemBK8LJCJISkRZ3HOlOLNZKVgtWqkuLM3QHVNC5NEQn26UkKwfMESJ0waxYJb4GvmwlgRA3ZiQBxMOgCUZZSnHpPHDPSpAa2k1Bs1FkEBBYDEAS9QnTmnuytHFmx/CNsGQyo5Si94Jp6hOtMEr9EDK31prQPPJF4mfgcgOSg1Qa8s2BDMUioRgMTYzIbEPqjFSYUbhWZZoFEGvNJQpxq6EsCMmqGbkpjubOg7EbiU4rW0CoGoWm3ko+ylE8fINjvTmPPjOW2HdgM0OSVZEXbAbcgTcxcgu3C5oHVD8eLbwWWbfWqbLavv2vFzzpmkb7Ex1N5Qu2l/hn7bHKilMvRO5fp8EVpzF7ozHNuXS3OnmnhdLQ/YDEswBmRCoPm0lNi7qYadSxUW8dCUuUPVEiOMSGMGf4EIYACCUwpCgWcBOwpTQwTRSIOyCQvq0E7D1AIpa6BYhM8BYL0meC3VYNw0gK4l1CcSNlEeaRTHDfQpidZHtAY+zyBKNL6kHFLvAn0HubtBjz2ELB54SYdsUn3njfA8TDa0wLdxfnpuWcAeTkHOQ5/WkmzZM6giKABaEYqCpJ1IRRDgXNDkPEmXKgV4gm6A4lhB1RAXZEbQFoWbt1OCrghsCNwEikRriWv0ob/d4P1ttrucbaW7f9vnc8iYSaM7uXQBr3vcpQHYkPTRaX3PcIjiTLf3aVR9bfTJNs0ud+xQG0PX7aMsx/Y3p/XlNM0+TTFt5zbb74YKCm/NrQlszhtaSxC6os0KLrMSPJFam7HepjeiYbmZAJ8uSKbGAETRQQUxFaIXgIsOLaxDPwmwAdyANYhxWFwDgGrQ2gPJAzyXeUonWQa9AfyMYGchI1Uh16qvAW6mMXtI/A9KRTBBYRBWzIONATikT+wky2/n3gsM4bsb9MbQPzn7T3CX3Thu22BI7UDdQPWQgYQ8Ar3JaIM0EV4oTWBjoFYNqIH8gM4BDaC0gl5JHk1jlGiULDQCSPh2X0n/9Eq26xprY3S6mmNAvDlDjA0AxNx7URsdcnAca4ca0vDGAt0OgLhLfFxutR3buHQ5j32q753re/ZD2mEfldn93KcN5frYbbfb5ljtsE/GnNcHdGkfu5/TfubAbuxYP4f0sQZb39vYl+SYDVse0IYmYFKCZ5M2nZjX1DqpCLAF4JsCzSGvbf5+DVh6KSYUAT1uF9kcTCNs1gDnDeAnoXt10NaCtzgg2h9Zaa/ZJ9g495SAnUoJNbvHUjbNeKipAxNwcjCBL1SYn0oQAzok06AQ/sAcPDvd5iL9MuTuBj32gCrW3xVtOjz0veR9k193UHXd8bseVRttRmNsMDh7BuAAa6GOlyhcyG9XhQrvWsmPrKSKuKoctAkuwToq+sGjM94Ci0eViqsztX6ZIbvDikqoBXmZ1eALs0Fvxuf3QSy5SaQvHirdF+XcXpfnkT7Kt09rHqLb+oBkiGIeQymOucaumuBY7bGvH7nr9zELXTvhmEXbbZC+92zUQq3XczowNESgogBPSvhJIUHnhdTSZBJHlRhu0FKXGnCl9EmvZDEc6c4IeLFetXhthrmEw/fgU2DncpBWcg1lgcJRS5NSpDNLwM7W5zcHHn7fwew3mM0rTIxDaSxqa3BLO5yqGcBawiJcgYnz0NYLC6Z168hCnjdji4Gg8e34AyVyd4NelPiixxevj07q7us7JpUIBgn4tap9R9tbl/WgVsvkphEWom4k23kSBtG248TDSdcerAlqGvj0lEUlAIqFH6+EumANxHx5MVbPa4ImATxXKuglsBEkOjThp96Z6bPNPbfcpLrNXnUOGUrQmzvuPOEHF+zgbscMacrdY3IaW9fml2owqeTov1y/+gA51/+h3zSnCfadn9N4u+9R7pl1+3uHqcttsrN3L7DhFb0RgE60YdeTpNEFeKLDYjZ4ZoZQJdYENwHcBML4NABM8AmYCP1oToWaBIJ2p9ZanSvlPGXR2vqYATf14ILhvDBEtFBwk0BxavEF5wnauczuseTsnHuU1yo86doJrk1WmJsaRnlYr/BwuY+/8ArLagazlAwwuipAjYNqxPmFnJPE1iryt369eFfqCRycnso2YOtKbqXZt1pMbXyJtjeYrSXx5JTitvJDSi49JbnpKNCelHDoMbNKIasnVrLiYg0pPjv1MI8a4Cja/4TGIB8M0pahaxYvrJgxPWRJb/PeBRDMZjvIeWf2UUu3Ubqebl26Mpe4d4xcmqa3bZIdApOxtGb3mO7nHBDm3utti5Ftv+1FAGUI8Ps03jHXHTrvsvqevWz/+5MrBptpYPN71/4fRam1Lc+IMxqXJiSO1mtbm1qDndj2GMoSdE0gT/ATDt6ZBF1J03YOxBg+QM6zexyS4iddM1LVhfYsiBh+acBLBSihPVkFBSCdMsxaKShLiw/Zv4WnTY8wURaeCYoYpXKwXuHdlUZ1NIE5VTArDb0yUKuQTSpquZHu3KEe6ja5+0FvaNAPHX+eSTwBvzNJU4F1+ALR5osc6U691gQ5AChrLas3rSTOZa7gCoT0QVF7Y7HbTTzU1MIeEFSthX+PdMUCUBVgVsETq2ao2ouXqA2FIIdW6jktrytDq/lULqjljdHktjmtDLV9KTL2vRljTxoCuPSYProyB4h95/fJrvbYXa6RW5AOvUd9C9LuvjG05SUv0oben9Hxn9vmqbiQju9NUqOzdVwJVRLshM54YwJAzK+pKgE7Vct3X4i9zRdJ/4K2x07CDsgFDdAAMAxtgulGMVhzC5a+BNy+BxsPlF5OaghkFaAYRnlMdYPnzN6PuarwF/UNLHyJg2KF/bLCZK9Gs19KUewTQjEJ8cuNBTViJuJU+81UaTiP3P2g1yc5iiRu724baiM9bqQ3p2QYYEkCmwZ7RwN1+M+lgdsr4OZGXuKJQjOX5NFuFi7tANKAMyxenKcFyAaD9Z4HOQJXQBuX54Wrj1XUKfLkPlRF7su3Gf/nHAjuoHSdToZK/JypQfaBlDGLrz571dD+bdfstr+tH0PAtO36Y8/tUrC5Y/so2fT8Ido2d3yf3AbqM6fN5ZKXD/YlF6IQc2vGhbOi1hGNA/DFv/Y6DmHMh0sEe74vGTqAXvTwbiVoaNGzE4pFE4v5K6K3pma4RoGXRo43wjyBZf7huYWeOMyD3e5kOUG1KKAUozAOtTf48+oG9nWFEzeBDwg91RYH8woPH8xQHxhMbkoQvFoZKKPX82W8x1ga7RI0vscH6I0ZSEOr4TGr3GCj6wLfmYD1dAXCHkBIChsLOkZ6McTQ2T2D6h4DO5V23SSs0Gp5mdkAlkmyEFkNb+RFBgDvxLbnCzmvOIGsjDiUCqkcaNVsUpuxa2ni19RZZ2iSPK8WceZRrvuRozD7jt1lX9r2yE7trr1tXqz/GeQm5/SdTY/J2bq6Wk6f/S/XdpfZGLKdjQHR7r12TQJjgDO3rY++ze3vO2+Xa15Qco4rQ983+pEyKioBu7Q2XjwljGc2Mvd4I9qeLGoJyjE4hDaxDjSnETdKaqiNuwMggelWCCfWQS0kOZ5LBilZG1MMa7CAuiU5gXVFcDMvznIm1Am1kiCfjQczYa+s4TzBe7kn6xSO6ineUT8dc1OjVA5GORTkYZSDVh60b2H3NZo5YXIrsF9GyXyZUJyAW4cweI8nbsjCNtk2IHah6HoGTjYtThf8PEt5jo4NzZuQgzOkCpLs5gCW3Cac9oW80Mqtk8b64LBKVl50u+dBVvLikZfKDrpyUCHjAVuXTHqJttdHZ+6q3fUcv817rY8OGrKJ9IUjDLU9SoYotaFjz7t9yPY2dpHRBbMhABvq1zaw7raf+5wCX3rutj712TK7tr+4bwiYhxYu59D2dqXNd6q1mPMEj3MGhcW1kgUzh9SFbqJD9YR4UfmLSeojpWlnwgDphtoUhW0QumUotw6Hijk3HSClhAhQPmh8WC+8WSHk5JT5R7twfBHvg1E5jcbpNlWi8woPne4HgklhWjbYL2vcM1lgYUsQMcppg2Zewu6pEAwvGh5rAT4iugi+ZeXuBr3bsILbaLdvMsgYnze0vVj4MR4LySpOFFT2mMWFxWvTrDxiLEqblsxJwCkr0ewoUBGsADf3gCOYBYGdvLTECPQmoGsPvbCg0xWwqgAfbHo5amCbHe8CsktcHbAGubHa3UW0wC0d2v2cbZPqWKeMbf3oAsAYW92Qhta91lCbfed2AW7bvm1979Nc03bS7bl9XTnHb7qLc1Q8fpByb+9V5oeNeLyYeSUyL0qLt7dSks2J1mWClGNJCB0BD+JNqWxgiHxwjIumQSf7dM1QNaBXHBJbiBOMXwRwJAi16dGCHpgkHWLJ0i7QBrfHEAffaKxWBZhFy1NqDXonSwPnCOwVllWBampQWQNF3B7PhuHKkHu4UOAiMGPtA+48T6UwlEJ4m9zdoMdedPH4eZsM0TS5tjeu0xk0HeA7k6UligtWYUCoR61B1gLWgayXMIUltTw8q7UxGUZeZk8c6uVBqIYTBV1LoLo3ADnC5BYwuekxueVQ3Kygbi1AixXY2mDT85taXp9cwIa3bYWbmxRue8zcRe05Y+xGY+1huTaHaMFtNGZfP7qAMkT757Z1NbY+u9oYjTjX577r9PU9d/7QM9v19+45p897WE45m8N16yIvucbGfBEBL4Bbaw7Rep2gvtRwMwU7I6hGgC+NqZMFtHhsqqUSr8swtMiJc1vMrSlZWAiNTsDTiXanV7GDsZ8AHGBO11UX3BTwkyR0y3iUpcOsbKCVx0Q7VE7jtCqxWpZwSw0qxRlmVQs4KuVRNQVsLRDkJhB/hkMNvdRQWoVK64HeZDFaknPCrl0gUO/uBr3bJUMTf5rJpc/dOHhwxulcXpYQruA5GJE7PxwhJG0FPAEwayrBzgl2T44xx+uXj5VkZimOgeljHtNHLSaPrNaAt1oBjU0C0JMwha5N4QJgt01SsNuW8HkMfbmTrW6XCTCn+Yw5f1cNI902tPAassn12fei5Ci/IbvYNlDcpgluA9EhuSgtOQSQY6TPdNHDPHSTIUgTW6jN1J4XJY3HK4zk1oxgF/PwGg3WQm3aiZIUgwTQiqECrRmzMEGJp7ekFyNQEzVAhq6CptewVFrQgLICdA7huEbMK+RCMmktNCZC9qcIsu312uh2YH9a4cZsgbmpcdJM0HgFHb1rCGCr4D1BklERmlpqgvJKgwC4OcPOCM1coZzKPVOoSkPxmft80vxd5YkFemMG6thzgY0XOLXtReNz1KpYqTbvnbSjQ+WFZDCxvGyCbEgyK4Rqx40cE/Pb2blQDvoWoTxmTG46TB4NGt6yAlcV4Nw6uXS3uGoalzdErY18Rttil9LPXRCUy52NvxvT5qXJeTXCbdrONtqyD5z6bGFj2Ym+a287Zxu1OXSdMU4x3c9DWl/8PPRsute9ZNlGvQ96G/f0q6U1iSS3ZgQ8Y8RTM9jxoFXw5ownIqQglDjcGN8rdjlaJ5BuRJPTIYRJr6QSumh50k9lhdIUuhPtHKMr0QhVw222J68Jvgxxw1ocWbxBS6EWU4sbswX+8sFDWLoSt+oZVo2JXQYUb2QSa2oDvxJvGaqVeKCT+Cs0e4T60MCcFODGSj3QMtj24rvg+CpkIZVdcjSOkqGBnA6A7jU7sXuslASnkwJrKS9EKwtdaNHoStVSCtE4HasnkANMAD3VrB1ZzJJQHjEmRwJ4+pFjYLmSYPi6WQNxALxsRYW+Ff2O0pcarE+rG/q+a+mfSynoet6Js8/21P2eo8uHAK/bVp9GluvLkDbYd14XfLYthMZokduulX7fRu9ukzE09KjuDaex6zs2mzEo7VPOdh61OqUF8AojRWEnBjxZp1f0hQIxozjxMMvACBHBT4LdP+TJJA/oJbXzhrIIteyCA0sdklUAYM0wIQenrqSqAnmx9SnL8IagVsE2VwI2Or0omX84BK7zzGEyafCk6QkOzQqWNTR5aMWoGg1SHsWUYRsNWAXvCKRF+4MV0Jb5j2HnQGUJZqlQ7hUoFzXYOSlSS9T+tqJUnPsnfvyB3qXHbQ0NyHR7QnVmKzG0Qetejm2kyKxWAAiwMOKWzCwJpo28ZL4Iq7EqrtAAOpHwBGWB2SMO5WM19KOnwGIpgBcpzVwJoYve98Yh+TyYqRZ3HuDaFcDOHQh8ETtdDqhyGsguWlm3jZx21QcauT73AWf6OWeby1GmKeDmNLW++xrT523Hp8ed57e8gAzR7am9L0d1JgcC0GfCE6RkUAhRKEybTNrtl3BFmOA9oJwHGChObZuCLNbNi3Y15QCE5NKuTC4di7QqbFCT5CVzC3mGtzKXRE1OLwFT+bWTjEW4VswUxfBT2UmFhCs8vNqHZ4WTZoLjeoJlXcBa6Rwn+RQF/xneeHCtpC9ONE9fAs0BUK0Upo9pmGMDVTcydzaNePfhHHNZRx53oHdbZGgS2AH42DkQtPx4ToGWlay8gkcnGw+vVSgACQnWdAyz5LaysbLCy5Nj6MqjOKqhjpag0wTwUiqza8dLg+W3hSuMWGl3J4TzLjouSln2anuXocHtIn3az9DEnzu/T2vLXSe3Pae59WmbQ9fOgeSYe0+3bbvn7n3m+ta9h9sEcrkY0lT6tLtRlRSAThB6+KzFS9NPS7h5iWZu4KbC/JBj6BVBV16SL9M6MF3CEEQrQ3ByixVY5GYgdj6zjtdzEyXnBOqS0teVAAStsVgwVBWSXXgFr5VQmhBvSygGWQWuNBZqggfsvXjf5ACN01guJnArDdIMVTq4lRGtjgHnNRw0UCvxBvVYh1ywaKDipargSwOlInB2zUf9j3ub3P2gNzSQBmRwgt42OOPEMAR8niUKNPWWVFLAtR04ikDOQQEg6+FLAx20PTYKxamBLxS8psDVSzUGZT3ICj2qFivw8al4aUYbXmpP7K6KPG/+H6Jvt8gQdXkRjXsrXZmZXLeC5hibXZ/mc17JUZjb6Mvu9Ycm+yFNrntf6bldLWuIqswB41jJ3fNQP4dsddvOvQ3Sdbo6b77XMxK1PFIt4HFhwBMNOxcvzWYmgejKMUoI+MUwgtber8SpjdKfR2FdDJYAsiHml6gtV6YCTSnxv2hTGapGgNQbqfSiSBbXqmHoJmiTgNgYg1cn1Qq8KrEqClRTUTF5oaEXSpJmFOu6f3LvvP4e/tgI2OklhT/xDPUTDZ6VUlm9KOR5ewaUB/H555e7G/S2TUoD9oZzBy9vs6sALaBEwDmj8TW2TTXEwaGF6gY6eGyBCFwY6PAfoYSIqiX8gBorwGYduKqBqgoZVnwLeOeiM3c6/GzlgyjdFfCYYPJu24PS5yAw1P4QWHSPuQjY9VGe28Aqt6+PTtxGD24Dub7jx9CX3f19bY1pJ3duCvRjNNl4ndskQ3lgR9ue2W9mc4pOGGpNccKsqyfYKUnMmgkOJ06JR+bChUKrEsIkrvyhTY+W6pSKLOHahuAMQJqh62gPFGCLjJFiQjMP4QyhHaFDCVDr48iSOMacKtjDELfnJGjdeQaXHqp0klHFEooK8FoLHbrngIJRzGuUpZNQhlMjoKlYNMyQOL8F3lJi9nhSyJwXYxgd8l7zI+XuBj1geDCdd0V4ySvJFvxS55ZYOsM5iT2pKiAavJVuUxFRqKcFQM6xDvz/s/d3IdctaXkwet1VNcac83nen9Vr2b1W+LTz5SMh2oniRoReBEIQYxM6kGALOQi2AY9ktaANIgYP/ElUzIHkQE0IYnLSBMxGAhrjH1tDtEVpIhglHuxv793fF3t1t9293p/nmXOOMapqH9xVY9SsWX9jPs/q9LvaG973mXOMGlU1xhxVV91X3T+O24axSe3uRELNM9vB+7nfFAjG+x5x2bMJY41Gdna4AehzmtZaKdF4qfPh8ZghqLEHtQm+BKBxuyVJ0aCpunJ0f/w5BaKlPcQ1WlwrzXrX37kiTe+cH9fZ8y7bSidgepr3863iEIccl5f98+RgmZ7UADoEZYHZfcFpcQCgXcZ0YRkk54zp1mVjOdo5KovfPjEuML7fx4O1c0QXebBQNwR9RTC9gdwLjgz1wEBuJ2y3Iw4E2CcK4pYgwMymMQQS7M/30tUeN1LjreEaOAjO7nCkeQuHnwlO4oueBvAX+At6E2gbLGvKxrJmpRq6A0RuDUTEFOccUw5MTQKYY+/RxL+plKftmiB+ZhhxPPieTXdUAr7cyrlwz7mAu+H5SyKmkJQXa3pNE1yL5tU6UYblWoE0NyHHz7q2xxWey52Py1wquf21VpDKLQhy4F6jWe9Ry6uFsssZY2WNVs4Lnn73sSO7YOr18wAxrRjn0fSuCLN/HOACyy+Z0+FAyVt+k2GwnLOwaDs7psvROqMXC5ospGatzWdQ95FerA+IYVnbU0feP1R7wHxBQF/ZJTHtRLCG0EmNUWmMvYW+AsgZ18hbAU2Avha4OfaYjABGAXUjHB3L9apb9hVkn0LjjFgmQJtT3+I7vM4vNuiJlRuaX6S9gFn8gAh+LA9a3tjkLPbeOHJQVSJ2yvRGKXEqoyCLcCzpeKAFN4WUND6rnPVmeK4UUHqNiXhDZ/Ln1qz4W0A1BrywTKqOmO6Mj+eAN57oUxpXK91Yk1ZtNUdZlp5xTjNOAVsJ/HJ15bTdym9+ycIsfJ9ryY0BzPNANvemtS6BqmHta3CA5bcFHIjBLH52NFm3D2chJIEs74NxtBXAZ0kQ7rtwiaaFXurgTAzuGg3AhQbjvHw0W22K0feD43Z2T5nqnA4EvbWwCpAHAf1Wj7cGNlKRI/vzwWVkYCtNwnHfYTgomEmAjgJiIMiD8xEcAHVr0d0ayKOBGAzHDx4nF9nKYHb4u8Ni58UGPeM2RdfK2pVqTVITndesQmtJIKv98Tlz8vdMqwuPaX02iJJgdwn3fQ/PpHXT/86O5q1gVirTqjm2TKY5jbEFGOPrSjRdWKa271bTAlP9r+3F5bS/HGNQ6kvpWIk+zZW9RILrW/aeS1FZzupNZGdhQzc3/rUBJg1xnKBu5ayhWcmLenUwLpKK2/93Vpvq1oDVLN4Pm8OCWbbAhKMyfaiys7mS4GJ5Oo1SEnS3/FVHjuTCBjMWHBzDAiC3t8iWKHrLjuw0EHgDkfcaheYAGnDaJjRgbxUbld4KqFvioBuEGcA9fSv3BnI/AseBtTzLqdqstcB0t8gsLzboeU1PEL9YXmqTdmlA14CwBShDNTw8lnrxwaupOBO797Pzx0tBrkODmaR7gu9LbLHZSic1ytvmdlC+qK1cicLMaVu1ybSVzkz1OdbYSppQbp+utHBr3b9L9aW1jlK94buVoyZT7aQo2/jZ1PY1LxESRTrzvHiDhuf7lNP+/BaItaBJQ9wOUG5OEwODH8hpc0cDGg1oTvRqIWB4n+8IYANY43x7FTubmw5zvF7SbKziDVjkCHj2yChOWD1e8d4hWecXHFClpAE1GdiB9xZhxezrN06EyThjdQVnlc6xgWniY6ZnIOT0RsRuGAe+Vx/A2vsji6OF3E8QtwNo8mEf3dYPeMqHGc6faaO82KAHnE7m/vOle3eXDJxUW/GeXqqPYVmAsyAI4r+BzGlEMhrbWY68qN6m1EGF59RC4dxXFJx7DS+2BsBS2lh8Lqw3dW4N9RkeL4Ffqe2UtGpRYV21PuXaiO83ri9VLq6jhXGpLUxKslL7q71/Oef04rufozadpseaoJn3rsStQGcsxKBgO04aS5pzY4pRA0S8nSWJ41Ma1rjIIEhHhjnHpg8XZp1hiI/cwnSi0+CIw3+N147unBb3Ba+FkbaQA/9GYuD2RkeFysHHAybny8d0qjgCYkvQW8AOXuMkl+LIgfuEOXSaOvC+ozpo1vK8lbqf1xy1ST734IXy4oOel0sm3TXgmKNZ4vNxn8KBlHMOj/+GktIa10hIs+Y0vZS4c7XkmKX9uvCai6w010oJbOL9tHvQDJLttQBeSgtq1dZqmtml9GSpvVx9KaYgB4pxuy0LjVS5Fq079Xvfg6QA737qNaBJ82SOgUMUjhq2W1KU0cgW2rZXIG1gpcQS0d7Fx1Qe7GgBvY115VwMXwUIzZ9BzoDF0ZymYxpUOO3L++pZwccYYNl4hS0rBWuDkwtjZu2c5JZDnoGzPkz812dq90AKs6Q7UgeL/sZA3RqIg57v12t4szuW1ncCPOAFB7353sMJfe1kWhq44XEvPlNC6pyXFJWZKhP+9RKDVKg1+vNrJWe5uVaTSEicfiU1EdwJ8NaUbdlnS9VfKpPTMFKTeu4dCvuRA4bw/BqJ39XW69eUqz3X+P5zQJ5aBNSoz1q/cv27h0VVLiILV3++p3cm8fEgO/rSx8iK0/nfEgCaItaHiIMtKzFrYay90QnY+SDUesuO6UID4wN2bhcdMY0owH0xvh6ncYURXbzVqMasbdFkQbCQPmDGRFCHZQ/SSC4nBwbAUQP2yHUIbzDjq7eONj3y/qQYDCe/Hp2vn+04ZrGnOL2tQy09WkVeaNCz1v13iaN5vG9QGqCxxCASDzB/PjXwSgBgzbI32eJft0Ziw5pUX2qTdiRxnM3ayneeRNZMRvetDebqzy12cn9zdF5cd0oDWfOuxZLSmuI6cr/dJaBa60sOyFo1ybhfqYWBP566JqUB3hPY1YyvqlGd3HjLuhC5ZNM+hY71SWN9kAovfpLvOVCFUYJ9+hTvwRnntmAlYJ22Z1z8TW+kYnoL02M2TAmDVIdhwLwlJYA5ODV/OZ035H4CaQkxENQtnIZHHCnKMgBOVwJCC5fvDy64tVmAz1VJE7snCG1cXyXQSZDmfUwxTByjdOCOkdanibpXygsNehzJRyx7Wd6gpXVgpwZSKBmLy/PzjRpGWF+pjxdqdydGMLl6U+2cHYtW7Ln2Vi42WqjQL6qkQChHY+fKxZ/juu8iLeBVokdzfVozPmp1t2hsta2B3EIj/hzXkfp9Stpfo7S6MRQBD8jMGS7ANAnM2dGV4shLUp5EYCJP6flE2eTSBxnLGKXceJeLv56VHHBauySvPuoXXw+Yzs7lTOeoR2cMyVkZ2DKUnIvDTKG6SDAwFmLiftFk5ns0iu9ZDBpi0DC9hBg7TFdytv6cwS7Yo/N0p94I9k90zvTeYkWMBvYoIDpOqEv7I19qvoz39E4BL6G5tNIoxRc12teixTz45HxqsITncoMpngBiS1RT6FtYTSLsWRyo9QwQS5ZlQPMkmdv7WwVw9zFpXXJtbl8ptzfUSqOG71tp4o81pPBY3JfUdbG07uPVvqeeZct2QHwsVWdOcwu/l+j30kIk9awL0uKgfmdxMTZJiiWdkBTApoftO86y0HGWdCv9oteNqXAMW5wEnQYw77951wTTW+gdg5vcczkxEufB21jAwPnnWQhnjSkmZjqFAowmyMgdzGdhZ4t59isky3WRtRBKMBU5atA4QQ6CtbSD4vuR3GfdC44n3Lk9QMl7gGQY+Ob2jIs80wuITkAMGlIJDvtJBJjjxT/FCw96Jyb9QH01GUp8LAFuyzlPO5qTdk+AJHYRANJaYmrimBvNTLAZwMs6vLo+z2e9Q3vg35fVCFOGL4k+hsGzS/sfoRQnkNRK/74kBag5ai78Xiqfmpjj83H9pWtL+1NxHZdIDoxyi8NSmzlwTD23VJnSwiF1v/HzanlXGt+jNemvfPkiyxHcHwV7eHOQaaITwLPbjg1TPHUpWTOybt8OcNqdcFFTlAisMemU1twwoJlHEyAsjOk4ugoBtrewktU3a8EGKQqwg5seBgAgKIs5/qU3eAEYbAlu31HbxWkcAI0O0J1PHQCI2yPEMMH0CmargJ7v3fTEmdnFkvgWfo/S3ZeY7BwLVCiC6Jg+hbYcVlRfDl0vPOidaXq1VXtOSoAH4CQprC/jjqeA5+KcTys1lWTYsVRWYT/wpDwB7rCOM/CuWHyGTrkl4HtbXRFiyf3WtUkynmxrv0EJKFra8NJKzbVo3CXNqHYs1mxTFGWtr6n9t5QGGLcZfk/dT07Dy4HqHaWW27GanSUEPC9BkGlIpjThsqZbKdk9oXNBpxXBOH89v69G2vJxFwTayCWfnpUeLPgvLGYt0dOHVllOCeT5Thc+zCgz06TQhE6ALS5HgvVg5+uF+ysEfMoFcn6GFuC50IHzSdQoJx6kdcf/jKMyLWEGPAAuFBpBjwx+SgDW73MKsNHo2ONSeeFB70RriSnO0t5AYaV2kvMqXQAn5rRe/A/tgNDGRjYpEGmRgpZ36pvnXgz/OX7x/HdfxgWp9vebdJBPPdPExBkDIFBZQV86SbXSnyUQSp0rUXHh8RZtJQcGrdpRrn8p6jB3XykpAWeu3hplWwLRlnZz16UANFVXrtwdpBZ+LCnxokoE49CnEhLEtGbfw257oO9gdt2ivSmCUQJ6w4Gn2UndaU3aa3q8F2flAiDGaXoA5n0zOggGN+KsClZZoPO/HQBl+dEZgt0YkOJzI3pHebIVp1FMjYJcFBbL4EThHOKc6+dHYckZ5bj76oSz7GTN0rtH2MAWZY4hCqdhCoA6NrwBAOoIgAFNArTtYA9fxpoeEGhVsdN3THnG+2vBC5zVlnL0Yeg/4sVbFHmtCsCc5iccLKUVbXiuQJHOQOWzMIQUiqDy5KyDDMRSLhnWAwDMOrrflWKbb+AOk1SLFgbUtaaStuYlRW/mNJ7cpJ2jsVPAkfuco/1SdV0Kiqn2UteUzoV1xVpjCghLWmGuz6XFbKoPK6XETiTdcxoWQryY5H08u9vA7npYJWB6CSsZ8Kwg6I3A8FCy35u2nMx1tGyf56hBn1lh2hL0Bph2hOmKQcN4ClOA/5IFpEMq4ff8AZCF2BqYUYCEheo1rCGMWw29I8iBUxyRy6puB3CCa83uFNBuTvIKAN/kyYLadhK293uVDHyAM5Tx9KnXUIWzAAXTtPNzU4CZCAIcj9T0AkbbL1/rTQDn1JyXnMbnJWVKHGpHGW3vLN6lz4sXB4SuWVC2UH5hRJe4/RCUXR8gpTN3XgB+Lh+8nFYbwGimJrTm/YQoiHUW/AI/m5PnULDMvJjibNXoUtpIDDYpKiwFUqcdL7eVo9dKwOOva9GscgBYqjMuu4YWbWmvRkHWtMkWKfU/p3GX6NGwXFPzDhwqBi7J/pb23kU4RonBTrJhh6cu9ZaTrxoFSOM0IANYeEMQp90pYLri8GHTFTBeM9CZ3sI8mCA2ejGEibpEwoKEhRAGcmtc/yyMFY7yZAtQMToH9yMDLgCONQYwzTj6/b3w/rEswoXbo3T3ZqUDUe2MZmBnmtYDOVuYMtCyvyFBCMtxO4VLwSQIpNLPuUVeeNCbpTapRjRjksL0PxYRa1DA+Z5ZuLqzBuTeKP+Xw4mJRXvCKVCeGN34+kLKM+VPl6gHoabn9ghICrdXwC+clYK59hDMfYBbrTkJ7TCCXADXkxRHQQ7Ak9ieGZAobey/rS4KrZocd+QUDFP1lCbRVNm4DzntJexX7lytfKqfqbpq/U1JC1CmjrcCVEnb859DjTrVTiudnJIGWrwl40eV3gzGrrWWZwUhFq0onG/cJM5AR47247iV6mDh41LaABh0T85CE5h2DErjA4vpkeboKspi8+iI3YZ92rQlHI+L6mQNQSqDvpugnVU4kcU0SZAwoJ4jwZgNoEeCPHJ7rHkKiFuz7K/FTuLGAMIxT3K5zxkwvWGOs9AhA8Dl8uNM6XxPZADsWTEll9ndSAJJC927pLq7L1d6UwjAIL33NJehEzrz1GAjAXjGMJA4ipCsWaKwzIAX0YeB3wmRyWpPXCRyd2h0Qj8xOvFOrc63h7qOLcG8+bPiTXHTy5k2odHMQCmOE8SgQccRdBiAwxF28rQFO376gNcQYk5+e6L1JSamtRnSq5KaoFJAEZ6LJ84SFZYDg5TWVpMaSORovFS7Ofoyvv4SzSnVlxKQramnpf3UPZZo41hSv2Hrc6j8jrV8kK0Wm3N5onnMgYRbnLLrgnc74Jx1hGkrZoMUOWEOLA1ySWIJ8IYt5KweYcHWl8rTmQboDaQ02PYjrroRgiyeb3ochg7kUgf5c6OW2I8dtOFwY0YRjBHQO43JsOM5gy4DD2tsAlIEz8XPvdrNL96dwiXGPcsyMz8cYNosfnoz8PXOv3BiNwq+1jnSu8wNVhBM16a1p+TFBj1jQKTqlpInWp6bxOM9uxOK8JwmDEGJwogJVpxmQZASmIijBhAt+fAcoJ7SoIl+B+Caol8p+AwSDHi7LczDHYZ3X+PwsmKTX7fx7QPA+qCxHEG9gzwayNsJ6pkCdQp0HIDjwNqeNpzNPeTr42cZ9f0+YxFGFbsbEKd/W8qXgCtV7xrtqUXzyGk3vkwN3OL+pdqtaWi5vb0WQF6zB5jqS1xfqc8l4KvR1K1yAcWZOrbqPZ/3u/xziLZHHO2n+2W/a85wbu1i0g/Mob58FBU5AHbvXQsEzNZAdIYt+42AsYSdGmE6giSLjWIUedgd0csJT4ctboYO2ojZB10pDS3VvCeoNwRzgNuPsy5hraM1KZoLwq0Up0R4Nws+jvm8B1MAc5aFOSuExQKGDvAsEUznkuC6TA6XyosNerHEGlMKPEKVPDYA8Rqej5Sw28J2iv1Rxmm51qvv/tpJL1qdIGAQsIcjIAwITmty50O6sMUR/oxe9Xt4nspUCna3wfDua3zm/7HB/i8ZyFvCw08S1K3FtPNx8QBYmsMBkZbYPJXorhXUzQbq+QB6vgftj7A+3A/AoO0XCGGqI+BkYlwNeC37bL4Nfz78nqqnBmxh+dLkF5bx4BR+zrVdAoya1pICwdx1ucl7zV5eqa81bSvuf6qunPZceg5rga+0YInLhuUvkNgq+cSIJaflxeLM+zknHtOXfIytM3W3jE8GP8x57ADA9IvhB2mCGCw6Q/x5IhwFAAtoLXAYeWq3lvCgP+KV7Q16oXHQCls5QZDBQXbYdhOeawlrCVoL6EnCuw/MAax759jeEcQoFlDjGz396z7bGdgcQyVYo/V7dz5XoI8Vypaq7ExP2lltGj5nFM2+fFYQpo2AkF+moMfvTWC5mXAOP9m7M+bU6seDB7BESVAS6DvYvoN56Rp6q5gOvB1BB28/S0Cnlh965Oy+nusmY4HeuGSHmi2dwjZTdGHKkTsEvNnPR5wCdKdgdh2Gx4pfIENQ+yBnFpi2GB5xig//QpMGm0f3hG4nYDYSqpeQTyTo6XNYNwkTJANfYDA0U7SF1XBVclRjbR/ttLHyRJY6l5vQc5N7Cuxy2kaqbHzNGk1tTT3x9a2glpIccLUCe0lbzgFf7rmsobNjydxfK/0el8smiQ3/ujbPkkMTcRln5Ob3ucRkIQ8GZAnSWTNyhBILGAZDgMesPAI+yLSChdEMRjDsCiCPBG1pVi61IUhh8Kg74KV+D0UaxgoIzgOEg+7QCQNBFtoIjIPCdKtAR7nE5PS3Sey3N/vSedbJG+gYuygCUrCj/UaxtaUkmA0b4FhJcx0+oLXP2ycHzCmN5oW64bkKBktMUHfNpfJCg96J1ABvBorERCGIDUH6HnbD+2P6usf0sIfeCIiNgFKCI4u7lB+2cxu23tJROTDVBugUa3QAYCyI7LJPlgK8jCQtNf39EXEmYSlgth1IWzz8pMHuswQ5WJcE0kLoJc3ItGPOnDRBHtj6C+AXWPcCfSfQA1DHAQj8bkJDGIop2rjP9+2MDuS1vNo+We66+PoWjSDWauL+tezF1cq3alw5oFyjKZbay31PfW4Fv9TzK1HKcflUX2ssQCyurdaQYxe/x7lYvS6DAibN2x9QHMJrNOwQ7vb4vObD8SqXvT3rNDmhLcj6bObkoqwQ2zfcKIzCQkoDJQyOk8LzaQNBFhsxQQmNDTHQKTKQgp/dOEroQTLgHWnOdzdnc/B+dA50IR3bNfrAnX4fT7CzvRK8FycJesdBp1nLo5nWnANdEwDJmp5RgFVskUoTA+EMvv7etYW4wxzzzgG98EUzlv1hAqOPM9eDwLoISoG2G9jrHfSDDcxOQW84c7HpCHqjFiCdDMyGOW+aLAAJu+tmDU8cJ4j9yI6cCAaOZUtPC7DWFIJHbpDM92ZOaVgSIKXYufXhFaadBGmgvzHo9uzfMzuyKvey+hdWAoCF3sE5wzrgI0B17qXt1GzIM/soBD6JOYpzfrbeeq0UYDqY7KpAWdNIcjRXbkIuaTopzSKevHMgmdNwSqBdov1SE35cvgQ+qeMlyjKmEUvUZtzH1D3l6g/7XtIKa5Sl/1yjNcO+FW8l/w427+cF52frTWv5s3MR4orY2dsi0HZGA2gXdQWYAc+b+3sHdDFaNuwQy/6eGJ2WNBLMQeKAfl4ja3uNt8QOGzXhYXfAu7fPIcliMDw3CmFgtIA98Dwy1zm55LQ+pqfi+XCO4OJpzpDiFILjbCoBIz3wubRHiv0KwywLnq6d9/sEu0tYAqCckmz4ngHMe5yk/wL0TkUEkUWCKCknwGEsU5RKMYBICXO9wfioh95KF3MOHCBVAT7SuSVA7wS/HBOHBtIbwvBAAJaBZ/P5Cd0XFMSzWwY3oxk7og3fZNSWwMp0uZ9osLqgtbbvoB/0GB8pjLvF3FmOHmiXFZo4AtSDsx5bwHSWo7a5PFxycHsI1s4UqhX8VpLBTHHGz7lGcWYnkmACypZZQ1+27u+ktJcUqMXX5tqNz7dQjuHnS/aZYtDwABXTj3GZXL9y9efKle6/pHWmjudAOwf0uQVIWM8d9u5q0pROCFjGcGg0Zw3s5LZCdO+syjH7ns1xNuc9PMEKXLcAIeBAyU38QgBGeydygjgK6N7CHIGD6HBEh+fEfnmbzYhn/QY34wad1LgZexwmhUlLtxgHxFEwzThw3j24tqxaNDXdC0iv2QkC2WXfDpIDT/vn4GlcKwjTzn2X4EU4mIGaaUwDF4KMgdHKZf5ibdcvAizE5ZiHVW/Hj//4j+Mbv/Eb8fDhQ7znPe/BP/yH/xB/+qd/elLmcDjgjTfewCuvvIIHDx7gQx/6ED796U+flPnkJz+JD37wg7i6usJ73vMefN/3fR8m7yO2RvwLtkLVnZ1EQ8DrFOymg+kEpmuJ8YHAtHX5qtxG7rQhTDuB6Upi2gqMVwLHd0ncvlvi9isEbl8lPPvLhCd/ReLZe3sc37ODfbAD+s75rpw/6nTcTJrPLffpBrQQi3WpkrDbDuMjhcNjgWlLmLbA8Ihw9P8eE46PBI4vEcYHgN5Z6A3/AxbqQm95dUXG7TV469Sgb8nIL76/hUmmujIuTZKtk1dmX6VY1n+ONY1WbSM8F/6L669pS2GZuwBg6nMJbFtBqVSmVRuMpUQ31xYguT6+jWAXhtg770pBO3QaHgAew96QxdkAhPnqjFpM/QG4vHIMMLzwds7d3rIRONkPEyPTg6QB0gSMAvpWYToojM97DM96PH+yw+feeoD/+eQx/j+fexl//vwazw8bjJOEGcVcF000pxtaAl67BbHf05Nipl25QLhIh4sjitl4xVOzEHChyJxT/QNgeGgxXTlHeIU5rJpV1qVA4mPcsAPFS2MbY6Wm99u//dt444038I3f+I2Ypgn/9J/+U3zLt3wL/uRP/gTX19cAgO/93u/FL//yL+MXfuEX8PjxY3zkIx/Bt37rt+J3fud3AABaa3zwgx/Ea6+9ht/93d/Fpz71KXz4wx9G13X4sR/7sXW9Tzlyxy9mbADi9u8gJQOe4hxWtlfQO4XhgcDwkKD2gDrCmRPDrWgEyFlZ6S2HAZquGDSmawvTW0wHtqZSewV106O76UDHAdbQaTSTyv5YHAeTfQBPtT+z6zA8YGsrMbHDqt66wbLhz7DA8JIBvefIL/ZTxalERoI5sUJD5FdzPomQlLM1p9W6uNhoTi1UMkRokZSWlaI4a5pgeC7XnxT1F2shJa2qtq9VApCSxrUWeHJtpo6XKNN4wdCiPZao6hJ1nKNE4z63UNd3AMdcCq0zOdluMfOePhnLASGGEXRQoE6CJgEh7BJAmnx+vMVa00gHPBrzlgR3gKk/OQDSxcy0B4LpAWuYXoQFSAvYSWDag41VlAVJA3KZzu1RQhwE59TzmqR/XBqgINeeHAwneA0pRmvd+88LY+NSCNmZFvV18UqbNccFwDkotmvLwoVdw+zOIEae49jwx0IMFyy2nKwCvf/8n//zyfd/+2//Ld7znvfgE5/4BP723/7bePLkCX7u534OH/vYx/BN3/RNAICf//mfx9d8zdfg937v9/D+978fv/Zrv4Y/+ZM/wW/8xm/g1Vdfxdd//dfjR3/0R/H93//9+KEf+iH0/Yro2cYuYXFSEgdblk7j8oDXKU7tsesxPdxgeKQwbTm+3aiA8YEzAtnygx9GfqmmLTA+BPTWQl8bWK9rC8CSgN64zdpwJeSEiE4Mj5Ih1ICk5aa1lp3l/bXaQg68GtLBikr3rNUNLxnYzkI9HvAV73qGp7db3N5ewxqCMUC3Jx4sB7i0InPjQQcF0xHWnPoqhk72mcwKd/bbq9Gbvn+pYzHdlaK/chN3qv4SsOUm5DX9zoFFCdhawDslOUDLacwl7bemOZfqqdGu8XWlfcvwb05qv3VG4gDqTft6vuzsz+uM2LRmJuVwBCkJ0UnYnn3q2BE9oAp9t7WFsMvilM34XZQSCzZcc5aP6ta3BxeYwoImYvAzfK01AnYArJCgrQaEBY2CQdPRmmI6BRoxAeroAOeoIY7TbIk6++yFef+m4LNhC1OoBchJA5CLZuoDWdvOuS640GdyBNQt36MYLdTBQu01aLiAGfQ/z8VXAnjy5AkA4OWXXwYAfOITn8A4jvjmb/7mucxXf/VX473vfS8+/vGPAwA+/vGP42u/9mvx6quvzmU+8IEP4OnTp/jjP/7jZDvH4xFPnz49+TdLC7UZU4jGzisSu+VI5/pKcY4nyVaOh1eA279kcfNVBodXLA6vWNz8bwZP/6rB8786YfjLR+jXjsCjEeLRCOw0aCTIA0HdAv0zA7Gfzh3SgcWaNJfFATinPf0xKRlwHL2ge6Yyx+sl1YjZMPBBAOgMhDR4fthg/2wLMQiIA4OddQNBDhzYVu7dy+yiK5AUPIDjiSb0dSwEob6zFWdJKwj/5s7X9npyk2BM27VqCDU6LtWvNfRn7nzct9oeXCsVWGo7dy53LEfDpq7NacStbbXc2wXaXjNVH+5pR2PfOpCwkwYdBk66OgX7X55xcVoOwHv0DGy87w7LgGf6RRNSB4vumUV3w8CnbgnylqBuBeSRII7MQEGAA1ALAGRhR9YAvWuAPALqAKg9IA8W6sZC7Rn4yLh/k2E3rEmfULQ+CgunH3L36y1SsWiKYrKza4I3mpktNV3YsdmF4QiIgcvL0V9vsjGAW+RiQxZjDL7ne74Hf+tv/S38zb/5NwEAb775Jvq+x0svvXRS9tVXX8Wbb745lwkBz5/351Ly4z/+4/jhH/7h8xOCUPTXCOJNwtOFfgAGochMJ6E37LNmesJ0bTE8trC9AbaaFzGDAG00dg+OePfDG3RS43M3V3jr8w9g9pJzUb0lcPVpwoP/qbH97AHyyR50GHjyL1GBgbaX0/yiC5heGDV0Tzh8xZIKZOb5AdBIAEkMZoNB9hBPFeSBnKUX/1UHoLux6G4M5EGDRr34Fbm2+Fl64xg3QOM+1rSLtdI6eZW0s9ZJObymtQ8xoMafS1pSy/UtbZWkRJuurat2X3FbtTpbfp8cxZk6Hl5bojfjcmfdane3SWZbKElo0OKiNWFk31+xVRAb6RgjAiRbX8uRWRzhAjtzFgYHiG6BK7SnG+EQUIAmwjQS9A7Q/bIPxxSiYR9iAJhcf46C54qT/UGvPbKWJ7RlY5mZ2tSLlheGdXRbSLwd5J6VA0HhKFLT8/4drAfCwAevd3t65EDOzWtzpBptOYHtF2tPL5Q33ngD//2//3f81//6Xy9uvFV+4Ad+AB/96Efn70+fPsVXfdVXLQVSobtyWpTfTDaG/038AliJxZDD+cBYadmeQxqIa4PNdsBL13t8xe45rtSA46Tw1o3C5s8l1C2w+YLF7nMau08fID9/w9FNjgM7qbvBPEc4CXz2QrERqJzdj7GwxoCMgdiP2H5B4/guBTiaY7x2gVwNeHV39CMEDHjTsqKTRwt5gNsPcBy910D9prqU7qXWHEybTp+zFeBVV2ICqyWVLU4ypUkrZ+xQ0wxLe0Vh2dxeVSttWNJUclpfK3CX2l+7L1jbr0sBWQmQc88oVSY+nupb6XeIJa43V098/qSKzP66e09TjupngJd41+ax7BaTFgCRgcUEOgwQtx1kJ0FWYtqxwZiYDIPC5BeybsKHYFx0YNDfGKhbHrs0CbfvJyA0ME0EumKNUAu7AKOhBQg1gYaF2oSfOya/V8iUIrtjGfYpnMzZIpjr0qBJgiYDGg2oE5BHA0Bg9AGmgdmVCsT3oG5ZkwMBdu8B3SWQ3S/UphxYS7yrXAR6H/nIR/BLv/RL+C//5b/gK7/yK+fjr732GoZhwFtvvXWi7X3605/Ga6+9Npf5/d///ZP6vHWnLxPLZrPBZrM5P2GCVYyTkxcMSO/reeAYJ9BxgLyV6Dq2kJKDRXfDS5PRAmZHsB1BbDW2/Yh3727wsDviC8crfObJA6gnAts/B3Z/brB5qtE9nyCfHzmW5TgCRp9PAgmwq4oxHAXBGgbRQQCdwu7Te1i5Y2tTl4nYhwEiHTh3WrfSmhjg5ZFXcP6F8s/SdhyRhibNuU3MMoiJ7EnWhrOMEYHkVr/Nzr+tlGIJPHL7XaXvvs7aZB5ToTlqNK4z1/814JrrQ3w/l2iIqX61lMt9D4/FAFoCpxxw1mjOlnegQZqTIdckNrQL9/YEAdMEuj1ACgHQBthKZ+lITrsK7k0QxMggIiYLcbRQB58izM5U42JVaZcwYORDeQmYDcF2bp7UvBCeF8TOMtL76ylnNELaQh6No2O5/5ytxTjGzT0jY5a+GDe3kJmzs3AsYA4cjQlQe8t7dqNlDc8vJtxvOoPv0bo+aMijhrnDnt4q0LPW4ru/+7vxi7/4i/it3/ot/JW/8ldOzn/DN3wDuq7Db/7mb+JDH/oQAOBP//RP8clPfhKvv/46AOD111/HP//n/xyf+cxn8J73vAcA8Ou//ut49OgR3ve+9118I8VAzbG2ZM1MMdA4QRwmqG6E3gp0W+akxZHjzA3vAvRDi34z4aXdAY/6PW6mHn/2/BGGN6/w4M8J2y8Y7D43oXs6QNwOEM/2HLx5HNP8vnvxz7S5SGYLzpNsDWyzayfOkiCe7rGThOPLGwwPJTofvmjgOuVgF83P8eXWbyAbfsmtZJ8gbdh8iqTPkmw58LSQgOGXjHxkGeAkfdJ8jyWH9DVSmqT9xJabJFOTY0lz8O3F7eb23mK6LQV0KQAraYKpPoVlc9KqCdYoU1+mBXzXULO19uI+xtpaqT+1ftZozoLUHNXPFnUpTd6I8zkptuTUBnQcINz47jqBkZTb5zKLZuNwRXggASD3k9MILWcoNxbKK4NaYDTixOrTSl4Ia0PQbh4RA6cPEsPi9iAGBiF1tCC3B0cOgGkMFsJhhgUpQNrAdpipxzmO6GChrFl8+m7svHenDs5A5mgWHzyNBcDJhVQcWMsURw1xOzCDdqGsAr033ngDH/vYx/Af/+N/xMOHD+c9uMePH2O32+Hx48f4zu/8Tnz0ox/Fyy+/jEePHuG7v/u78frrr+P9738/AOBbvuVb8L73vQ/f/u3fjp/8yZ/Em2++iR/8wR/EG2+8kdbmWiS0dIqdunN0p4+S4D6T5hh4/TPO1zRewwVYJdirCa88ZA3v6bDDZ/fX+PPPPsL2sxKbL1hsv6Chno+Qz46g/ZFT9WizGHmYYBUUWjh5ijCVeUGkEuOaJead0/hoGCFuB3QbyXm5DNA7Dc+6F94Hj+XG3ABwJsKAi9wiJeRGQA4C4mhcqB/WhnmgitkqjbQ+3Up1k869hx8r7eXkVvSx1hWeiyUFWh5MUyAR1pkDsvhY3J9WYEh9j4+nQKRGL9baTN1bXK5EkeY0sRbtOdVmXDb329bA8h6lGIMzFrcYTy5wnbZnJzDwGQMlAOgtbCdONDgrOcM5AJA2EIPmeMA+judGgtwcJ4UL+CzZDUI7C0krvQM7IK5YqxIja3lqz0Yj8shg59P6WEUcrxPB/WmzaHlLoM/Fypx4f1A4LdWnASJjofYWZGiOyuJDJorJwpKFS+YQZJZgTVMemVYV+xE4Dkv4swtkFej97M/+LADg7/ydv3Ny/Od//ufxT/7JPwEA/NRP/RSEEPjQhz6E4/GID3zgA/iZn/mZuayUEr/0S7+E7/qu78Lrr7+O6+trfMd3fAd+5Ed+5OKbOEvcmNGc5vNYTIpBBLPpoLcKcJZQVnCUkvGRxfjKhHe/6zn+90efx7U64tP7R3h22ABPOnTPgP65Qfd0grzh1DwYRlhjFlrT2JOYmyk5zfHn+lYKTWaZ4gDAFlQTr4DUjWaNbQNYDUzKhS5y2pz28fPALzNHOXcvoYvo0O0JShD0qICphzy6JLNKMZBDw8a8uhvwqcngXrW+tVpPCA45AA3LxfXk9qJyYFrTiuIyKY2nBaBK5WvaVK5vJfq2Vk8oJQ0wpbWVFis1qrL0TqT292rXnFWRDjgdG7EUNT/gbCFLLlelJVrqHMD79E+BTlvohxu3RWHcPGB4L85piOLAvn6wloPkjwCEhbAMkGJiClQeAVhiC1HBRiJKEaaBsx2IiS01yZWVo50BiR3CHYDNwZ4Dm4jQXcG7LGi2NyBrl5+RMO/RiZH7L4/kskWAtUhjOR2am4NnK1EX3pFGtiynw8hbL18sQ5aWIMnb7RY//dM/jZ/+6Z/OlvnLf/kv4z/9p/+0pum6lIK8ArP11ByCzOdykgJ2Ix1YcGST8ZojmAwvGzx693P8Hy99Dv/71ecgyeCzhwc4HjvIPafu6Z9qyOcD6PYIOo4us4ITEoAwDkhFFvhO9sZi51dPcaYvPOXTiaPG6J799mYq07rICB0WtwbvLGpoDvsjnPUnaU4WKTaSV5gAA56nhZ0REPsclvf0Lga82oRXW/nHZcMyOQ0p11bYp7Dt8FjpPlL9SQFXC1WZoglTZcLrayCeox5Li4McqKYALqX55vqW6lPYnxQwh5IDNX+8AHi1gNOxn16z9WbMRMXbFc7+3o5m3laQxsD2Phkma25MW1pgMuzcPoxYDGPcApfk7Fogj7yXRpZBlZPUcmYGCnYmxLRQmqQx5+LkTjHQkXFg7ejUE4PAuaLOAR+DFAneJ2RWyVuH2sVFQ9Kc49PTmWx1zprsbMDj2qSBbTAwadYsL5R3ZuxNYDa2iGnNmWZwEzhNGuIwAdcdpiuB4YHAdM1ant0Y7PoRj7s99rrD58dr/N/PXsLwZIPdM8LmqUH3fII4DPwCTlOQUNaT6QowvNlsp2nZxI76k9PwTmKI+uDZnu8OwNBKsQCe8zcMaYLZqbVjk+HxgYXe8gsnDoTuOUFZ9u9Te7g4npZXmgBrrlrDjhM8NZyT1Y7pqRV/jRqMJ7DchB624cuV2k21U6LKcteE5+P2a5pMDkhL95V6Dql6U4CT00RTIJzSnlPH4utLC4XaMw7voaVs3Oe4/9mibQu0Jif1sK0cExVvawh3fhgZHJSEdZnW5wD51vLi2mtagVO4lXKOYsKgxNrVnItOYtbuxLTMEUJbZ9XN+/+zxmWdm8LRWWOOnB2CQsDz20REzDpJyfPbCAgiGPBf7PUyr7m5jo1cMIMhvIanzaLpTcsWC7kMFUjFAV4h7yzQi0P/xPt4obbnX8hJg/YD5KEHGcUpeLYMCtev3OK162fY6w7/18278D+fPMbTNx9i8xmJ7ecs+ieaac1xmvltIgJ6hTmvFMA/0DACewMbaXvJfb34Xrz4F01Kt3nsLFGlxHStMDwUc7QDS2y44pNRitEFdxXsvzO9POHRu5/j5nYD+2db3kR2mYu9xdeJwY3bdE+6WgQT2kWGLLmJP/U5N4GXJAd4qXvIgUqqvdKEupYOzPWlpP3V+pS7Jtde7ViurpZrcwBYaqdGfabKphYlKSkAYIurDVdRWdz5NrIBqum0LORsqObTEJE3fNFOuzTWsS1+sYzAPw6c3aCXc5JW1uhcv838kffQXJBrNnSxs1M4G8owCImR99PE4Kw2fcLs0IjNWFi4BfgwLqyUEAx4FiAtHL0qWOsTjrp1TdHEc4yYGFxnDW+cGOy0ntu2lfRmNXlngV4oKVeFULzJsNZswXk7Qow9hyB7aGEfjfjfHj/BK5sbPBm3+P9+7mUM/78HuP6MwPbzFtsvGPRPR9Bx5JVIoOXRbgu7c0Y5/pgQwDCcbv7G4FHQ9gCcWnFaM1MMZAwsMViTAUdXccZNcmTQs+PCyw+PAXU94mozYJwkjm7FN4cV8xqi4+ih9UJrBv1ZE+j7TpLSzlKTXGoiTV2bot5K7eY0vLjOFiCuTfZhmVbgDPvUCmq1PrWCYKqulAbeAqLxsZbnnmu7pgWu0PhiYPNgV6U3E23E2xWzjYGUSwB8ITg/p89iYCxP/HxB8i/HzRWwnYt7qQIL7Akc3owWUGOfXLiIKVgyHDg/XpoMs0BH7YCIDUlmAxYANsy5aQ2sBog0MNKSYNYYCCNhoGAVG8RoIRZQnlirE4MLjOEdzye9GNEByzwELHPkhfJig541YJ04sUoPY8EFUVkA96I5LWx29B4mt6LhKAj91Yj91OH/fPYKPv3kIY7/9wNc/5nA1ZsG27c0G688OYAOzDGztaaPtDBx9nWXTJZD/gQaaHgLFcObs7L+r8FMOWIYoW415NDBeR1wTE7hqQoCiBPKWgLGhxYvPbwFAAxH3txjnxx2WheTC0l2COLrcQeW/nvL1JOf4x5BsGW/qkZt1ai2EtWXeqdS2kWtD7V+hfdToiXjvsflWkCtpWyun7nnkqsrXlTkwDjuZ05zzf3eJRZgpaQMV2Kn9PC4B76ze/J9DmJwnhQ5yV4iGOy6jo3FNh3PHT2DBKzlcRhqPMDMItlOcTofKTkHqBIwHe/te83N05bGxb4EAeqGNbgZ+GjpL/sHsqYlvF+e1/LCKCxOaVhoVgNriA3f3P0TONeoMBNnU1cC0jBIi0HPRip0dIl1taMxTdCecy+b58q1i8FIXmzQCyXkz2UAeI3C/iAG3S3HrhsGic89v4LWAsNnr3D1psDm85YTtT4ZIW9G54A+uR/eD04Lexx4C61TC9gNY7BSOdWYTjQnP2Bzhjn+WinZdcDF8FNf2OP6TYXxAef6kwd+oX2UlemKB4HeEszjES9f7XE7dtBHCTX60GT8T+0Nx+D0fTblPTxgWf36zzlJUp+1SSqlodVoqzUaYDzR5rSTkpZYA4Hw+hZtL1dXTTOqydoJI/W8avWUAK4EhCnas+Vvrq2VElPzceSgEOCy2l14T7lxEMbe9cHv+w5208Ncb6Ef9RivFUzPVtVqr6Fu2DWJ9sOs9VkpWLvrFcxOwfQc0WW8Ehge0Jy1QB14XHMkFO63cvOdtwYNA1zP7gYTKwNzWqQgPOFZYAoPfJ6enSbW9ibNwS4cMEslOO0QYQ6lSKPPJm8cder/6tN2rClGnWyVdwboRS//7NANLHSg1/yMWbKpLxc4ipAtHsUImIPCgQCzV+jfEuifWGyeGvRvTZD7EeL2yMYr2ml5Xv12qyVrNHCged8N07TsicUSvjzhwDfB4A1fSh/OyBi/Dwzx/BbbNwXUg365V2fuazpeMRql2GpTE/6vz70EYwgYxBweSAx2dhQVR/YBnK02XburuPTEpJQExBQF6T+Hz6QGRjkp7ROmNJccDVoCtlx/WifgXN25ST6lAdWozdrn1PcWgF9Lq67VVlv26UqaaPR+kZTZhVmJ2owXds3Wm+ECNth24VRnEthtYR7uYLYdhpe3OLwscXxE0FtyGQckNk8Vtp/v0H1eQtweeXxLBhB91XNatEeKE1o/JIwP3B6gS8YqD5gNWgAHgJ2APEwMPAC8Kx75qCpuH40mjSRTlRkH1tIMXCQEX+80UkgJGtwzcBrd7ILg50c/p1oTaJH3xyK9M0APiFZYYkm06I/NtADnhCM4W36AH/akIbR3LwBoL2CMgriV6J4S+qcG3XO34jpMbh+PNS1M07Iq0RaACSgM3kgLM497Nb0puHR8m17Fn/PaGRBNsPsDBBHUZMAermB6xPXDKHLOoYD6nML09JotuTRA47Ii7J5rqGfsgsEv5al2yp/zfbYxgN9FUoDDjawDoPhzjj4L20zVVQK2XJ9K4JerO1Umtxgo9T3V11LbKYBL3XNtwZEC0fAeWhco8TU1sCvVF9S1hoaP9+/Oq8/Qm77d+Hy4Ly84k4ntO+jrDYaXeuzfrbD/CsLwGLDOtUAMhPGhhOkIVxboBFg7IoLZdZiuFaYHcs4FOj1wAZ3B41pvCHLDIb/EyMZq47VgJshayMmADhODndesjFk0L793J0XgqmAXIPKgPjuqu7luYp9BTJr3FgGej8J6XNkTtmuec/zzXjS9hcouvEMVeeeAXk5ijc+tHKx1KxktZvcGGgwHYT4SumcC5iCgbgn9E0drPp8gbgMn9GmCd0I/yZAM9zMF+4jxSiUJdqWJKdD6ZkAP2vIvlABgtz0bz0imPkgSIPinVjcWV28Sph1B79gxXR6A7rlFd2ugbl2kh8MAG2qyDXKnPb0ULZmSFPjlQC4FGKlyqe/hsRqwxu2UQKaFBvXlWtvNfU/1ryQlTSsGu/B4ToP03+M+lIAvB7Ipza0Ghi2/cYOktLlml5zUNkVAbfI+HiewPr4kcXxMGB8B40POhUmaHcs5u4KAmDpevN6MnFpspzBdSxwfShxfIowPOaG17gGzMS4/Hqc8072LvDJhzsVHWrFj+mFatDMPdqH25S02PUBZs8xtCABLilMfOm0AGNjUQuCEHs0sqlPv+B0X0+9c0ItftlBTkRJzSC9tYIktheR+xOZJj+kz7Oyid5zAkHNUmdk9gY4jx9V0Gt5JmDEgWAEtbaZALqXpZemS8Md29xbSuHMt2vm1eB8fJRZrJ8uaHWceJujBRWIZgf65RfdMQz0fQIfjEizbLC98zK+Hfb6z5CjImpS0wJb9v7Bcrh9hH1PUZ+oeUveXm/hzGmKt/3G9pXss3WfqXExt5u45V0d8HzVpvd9cnanfveW6BmmiMXMLA6CwP+8W4FJC7wSGR6zhjQ8NzKOJUwwNAjRxBvIDALknyEGxi4EkTDvJKdGcu9HwyELvLOz1BLnT0IOAPkrYJ5Lz6QFQewIpyzn5FEFvJMSuc1aTWEAr0Mbmhe/8DgcL+lDC7RD/TOb5wSsgAeidacJ2uSY0BPKf1/j/ZuSFBj1r7BxZJTrBf0OaM4zBGf9g0wQcB9DzPfq3FEcv6SX03mVd2Buom4kjAgwjA4IHOxOEGcu4IiR98OA0tsgKcm228RPgMwZkeVVFXQeiDTt3WrbIVLf+M1MR5hnn7FJ7g/7pBPX0CPF0D7rZsxY7OjeMCgV7L6HGlsrajsXHY02xpjmmtJbweEyLhhPaGu0iJzUKMdevUj01erO0oEjd99tBc+aeu28jdf81TT6W3O9Q+W1yvnhLtY3AV5PEeNIbgemKMD2w0NcG8mqCUhqD6GAPgCWCniSGx4TjjYQ4stO53rK1pu4Z9CAAuzGQOw3VTbBGwUxMZVrpzgv3VwLTjkBGgGwHSYC4daHNhkXDWwLlm2XuPLGOp+Wvsee/u4gsLjWW3yIRtH5RIqLnNEeJySwuGuWFBr07S5Diw44jaH+E/LzAhghWbjA4S8juGVN+GEYGgjGyajTLC+IlFWB21uxCf7w1P16K7oyc8e04sTUWwAY7nQIdRygApC3UgdALzmbM8fY0xGGE2I+g2wMHy3b3Zz3nHqcRsga5Tf17lTXaX0krq5X1kpv4cwCYo89yFGup/6X+3Ef5Vu25pv2G9cXn40k/B6y5NnIaemnRk1ukXCApgItjbBYuPm07B9Rn1y0L4GkL6I0FXU3o+wmd0tCTxGQIGAVsZ6G3BL0F9E6AtIXuyGl6NIcWhLAwE2E0ClYL0MDzmAc8q+CC0PP4HR+wM7v3l8PoQE2ygkDWLuEGM9s1ZxL+NqnnJjILodwzDrU8EkA6MGOTvNCgt0YrOgnnBcx7fGxg4uJLHg4grdGNE8T0CPJl3g3unwxsrXkceZ8rpeElIq1YmzH1z/i4rcrE7CcFHw1BigX4tAaOTkMbR4jbDnbbQ3RquXfH15N3uTgOrN1pw38BR2tEm8qJfod/V4NfiZ4sgVaNDktRhS1SAtrapJ67Lq43dz+1Y7X+xtfWnmNJUmBSO16jg+PjuT6Wfru1dOkdZJWWB9Q1yZBdMmbWnNiAhDU1szHoOo3dZkAnDbQRsJZghIUdBaxkoDMSUHNGcXZPsAqAAWgUsIaAifi788+13tTbd0Hx3p4UXIcYJGjooEZ9ksVgnsPm3y0a3ykgjOeJlOV8+Lz8PJLzW74HWtPLCw16KTl7OQNO2D/QE5cGfx0wa0jQBhLA7nYLqwT7xtweTvbxQmvME3Ggk7LOrCVdXa0pBROQddHYKdhXtANA4wR7PAIHlVwZW/8Se83V31cqSWxEw6b623QPpQkptWLPaRXhuZzWdZcJv1XryV0XXlvqRwpIW7W8Wj9iyjeWGuCmQCt1vLTwiBcoKVDL/V4prS+up/T731FSml6Tj17YhyDuJoAlspLheLx0GNlmYJCAALp+wruvb2AtgchCa4Hj2Ls2ONKKj5UJcEQVpjctJ4cNttBIszO6fsBtyuOpNTcHpedjeicgJgUx9ZxWbM/XkFJuQewWwCfUpo/cb+AT1/L9yhN7ByLioPtejGVtz//2wrDxXeiEHj/LcF9vJQkSygsNen5PLzvRhgMyNP6IH6i3SpKSNR+hYZ+5SOYAA8A4zr54qTx3ABYrUG+dBX7Bz5zPMxJHgEj5CBVXnO4lhgnCHBExhx5YVIX3f2J1GmisOaOVe5ESFXXhfkz22jWTX24Sz2l3OY2qtNeU6leONsz1Lby+RPdesveRA7hS3bXfLFc2BZypczUaOaU1hudXSspBPRx/q1mZjFitQRNnDuifjujfUjjcCEzvkgvgGYFxULC3Euq5gDzA/TNzZgLuNGZtzgo7H+O9PAvbG2gL6L1kqtPC7bUxeBrljFp6AbFVoLFbmCArXEYVLHPYSd5S4u0Wa2ZK9OxeI3et5cT5+3kCfIIWwHPxReH9Gy+UFxr0gFPNLvWCkt9EjYAvFnLpcqzkpHNknEuCHzhulVOiNE992QLwAM40vFoEk5QmldpHiwrMe33zfpyvLwS6s9aCKuJnk5k0iwuNHGVVm4RS4JKjxsK/LXRnqn+pPsZthvWHEmsaLX1N3Wuq/6l24jZKYBb3vwSMuXZKUtIu43rjYzmNsdS3ksadKt/y+xYkRdXH6YTCz03O6V78whvAbBm5P0C9tceDN9nJ/AZX+H9PEqqbMNz2oC902DwV6G6AzRcsdl/Q6J5PsIqgtXCxMjHnqLMbl/JLAJhY27OaYJWF2Vhozft/pDnsIF9nMW0JgAAZCZo6yMnMLghkrXc5np8fKa/lhXt+AoA5Ab4zC/VZ4y0/thMDGZ8kgACSYs7Ofom88KDnpRoBPQS+UGLqwWt9wOlqZq5nof5aY2bmNLw4fl8ul1c8uJJhvOLPoYUUkM57F5sBl+oN+lukMHOTT2niiSmu1LUpSivXbknWaIIpYA2Px+VqGlKq7rhciv6rXR9L+KxKfc61ndK+c9fE1+XuKTxWWpjE15V+r9R7k+pf6toGKeXW88eKwBffZ8jcuC0QO3K4LnrrGa7/T4J6fo2rNzscXtlh2gIdAWLgIPLdjcXmCxqbLxxBRw3bCXSCMG0E1J5gekD0gBkZxJYsC4A6SM6nJwB9xamDxAAADJQgtgAlA8hOwGwkgA3kfuS4mwD77/nIUoIApTgw9qRBGGE1FsfymniXscwcSjL4XYXkrRvv2+id2y+UdwToxS/dKi0k9APBoiGd/BhzwsfTMFzh57MUQZm9sNgqrAVIcoOvKbXJ3MHEJOktQFN9zUwQ9+qTl/t+3uhSJp70cpNros7icy5pLzUwyz3nVqqzVk/u+hz4rulzCrBzC4ocWNW0t7itEo1cAt5a27lF04VSYmLCsdc0BgOm6fS85RyVNzcQxmB7e8TmMxtMj7bQWwmzEXNcYHXQkE8HiAOnUDHoISaD7tZgvGULTisZvIQVECPvfQkNqOfkkkk7C1ECyPB+oAHbvJAmt1fI2h5bfBsIaxnclORF/+zCYE+UAL5Pt98XP7OzjDIBIxU+85DSBAAp5qDcCPILthASOXlHgF7NqCKp8cWSoCAA/hFsypckvCZDmaY0pfBvfDwnLdHez5tOaLqV/qUmqrvuW5xJbjJM9aum2cUTZDxxR/UWNeSS5lOTHD0ZazalCToHHCkNqkQVpvrVIrl7SAFU7VnVQK1UR+v18bn4XlLlV0ptXDbt7cXtx3OG1/g8UIwjxHOF/knPIRNdiiEPLnOwaSVBnQSNBnKv0T8nl0uPIAfAGA9g4ATRN3CZ0wGxI0w7DkohBwAW0FssWRgsQMZRidYldR0mbjOMtjJNi+YXanie2TKR9XrsH53KeRqKi9WJrgM6BbvtOftEJ6Htl/GeHlB+OZOAoDmVfXZC8NSgoEWV95JqK3UsAI27Sik4bouWmMsHdn7s/Hm0ZE64WGoTYlwuBSg5zSB3PNd+3Ic1kgK3EiCn+p/qW42+TFHGpWeUk1YNMQWK4fUpsCktbFLUZCypcjVArNGe96gFnnc3s9gM+xYFvPfW5LOFpJTA4cB/PbU3+/gyEJC1wJ4gOgnZCcjBQh45gpQPnA/ikGObL3DMzWnr2pMEST6NGAPedO2SyFqCGAliEpyE+iggOgnbySUwNOAA7fT7yfsRAp7X8mIqswR4TtsjIRjwdhuYqx56q2B6gYP6MvXTu0Ti1VnRDNnY079cwfK5NMi/SNLiHxffc26DPlWvlyb6ON/J8kTTAma151rTeuJjOW2gRvOt0fxy/aj1M+yXpx5zgNYiObAq1ZMD79I95EA2p0WW6irRnHEbMcDlfts1mi/axpQvF14Tnz/pi+9PRHWeaX0uQwowscbnjDlsYJm9pOYxEKOFHCzEACjBfnlkwQYuB86vKUZ2VRDaQh/4LwCYjsMRmq3FtANoJAhNkANB9gKmkyAp2XDFu2LBUZsxCxZbt59kZQgo0ISWN28pkQCEnGlNqyRMr6CvFIaHHaYdYZoa9w4T8o4HvZovWY46ZHXvtEzSWhKnWlJN6yyBVIuv3hr/uJb6S+CWK7fap7A2cZW0glTZVLkWDSQuF0/qtYm2hfpMaWElyrAkl2omOSq1VaustZf6DXIaYW4BUNKyUxp/7jdPSWoBlGMRCnLJWM7u88Xt5sZZGFbQWtbqtAYgXZ5QAzvBWX9yDE7OaM6JYud/AGhioCPDOfPUwTnAO6tNvQGD48TX6CvD8T7dvpk8EtRRQEwS4iiA2wCo42ccU5nJhxYuAmya3gzrlx3spoPddjA7hWnH+UKnDcHelpsqyTsL9FbQU2v8bFKuEL6OuExt8zv1uXSsVqYVgFrcIuK+38kBnQumf4/c/k/p2pLWkTruNaRSuRgMSnRraQJP9T33vbQP1aqRrdHa1mhXtT7nFhA5mrL0fHNSej5hv0p1pt6PC6RlLOfmkZo/7Sy1+UdrWHIW3taX9RnKXb47Y+csaVaAg1ETG66AnCZnAY764rRAw2DnEYAMgSwBuwlaWoy6w/icoG45NqcYOs6+MoxMu07TaWSqTN/PKM14j88B36nhIHFiXcVhFM22w3S1AJ7pANv9Bb3Jck+ruHozOe2w8rIH196X3Fdd9+Fsm5wUc2Vyk2cKrHJ15fpQ6ldJq6ldW+pXDcRrNG0N8GoaUw7gU8dr/a2dC3+/krbVQjnmjuUkp+mvrSdTNh6fJeOx0Po6VU9VvMtQ+B04j9wCzImqyRt3GLtkeXE0pRVsmGIkALKYM/5IAD2Dnd4Cest7fuQjjQlHgT4T0OhAEzl3BsBKmrMx2I2EPYizqJeLIU6gucUZZ5bCSeA7cV8QznhHCEAIDv6/kxh3grVTCZhN/fHm5J0Femte+vmSdaBxqVaVOncJANaumc83PItLtNHg5GUr6tpK3n8uUVT+e3xtSdNZowXlQLEFlNYAUqmeVPup/qekhXqMr19LR8ZtlcrV3sU1mmCp7jXXZ8q2sjGpcqsXjIElZ+rYTHW6SE/ciN+sM4AVoNFlPp84Wwppgt4wUHlrTYBpzWlLmK6B8YHF9MBCHAndM4I8wjmqs3O6GIH+GaF7xvuEJ30M993CVEHanvYzJ7m4nbEIAesyw5teYtq6TBI9QW8Ac4eA05fp/V+qklm53YtkVuh3CkN0gZZWA6L5/B3Av3pPrRNUiebLSQrYcpNx6/5O64Qd1+VB7NL9N39tDD5rqNFS+VT/Uhpt6f5LGqa/tkaRrmmzZeFSe96tv3vLsTtIyfgr6R6T+r2sOTeYi8efzwYDLJGWrF20P+uypQyW82UeLcRkZ8d0gN0RhkcMeHpnYa409M5w7j0BgBzwDezi0D2zUAeuhw1k2GgG3oL0pH+RlWboy+fTENUk8oGeNVy5OM8DrM2aDpiu6lXm5J0FeglZ685QKFys/1592S6VC/cuYqmCcW0SzZWr9e9SDSu+JqZPc1pebR+q1L9cH9ZcWyofX9c6YcflUyBS2h9MadSxJtuqiZbK1EA2br90Ta5fLVroHaS0v960cAz75OsKQRA493Pz4DBOC705jBDHCfJoIIZTg5blWsz7emIg0EEsxw0gjuy31z0ndM+Z7hQTZ3Ggybrg1mA/vVRYsZyGFwJgLD6cY+ja4Fkq79Pn9ibJMKCL8RTML5F3Fr0JrKJJqtE5LtkjvISmWXvNfbVxKaUUXxvv86ypw0tqz6ekLYQTcW3iTl1bmkDjtlPtpGjIWLOr9bskuTItVO6aOlP3Ex+Lf48YCMPfvgRwLRpafF3qmtzx0rW5Y6XjjVIybmtaDIfPJ9GPk7RooZZlDeykQQPnw5Sd8/8jObsm2MnvkwFqD8BlZRADl1V79u1jlwcGPrV32t0EiKOFOujZQhSA0zRTC8AFqE6ALhON5eScDzHm65kmkO7mdsXImic4CVwxfnBNXmzQswZ3oHbzsmIAnO2xXQIsawdcbvCvbeMuK9+1ExiQBsrwXFhXqmxMd8b9Wfs8Sm2m6k0BVgxCJWBJgUqrpMA6BfYlSQF36tra+5X7XUr3U9Nma+347yXgCq9Zs4haKSljlzVMz1y+pIEbkbbsdOBnhWBNbhxgbwESAtKnCNoIyNECI0CcLhRkCEaxVaeVgDIce1PtPZj4CC3WaVMWcvCAYyAOEyfRPqEwzamWF4OdF7/X5//mQNDfl7dYHUbQcYS8legEudyBEgDB3AH2XmzQSw2Ke6YwanIxFXgf8kW+1ztLbVIsTQIlarEFvGr15M7HQJsDsDVS0zZzwFvq99o2c1pcqR+5860gWasnlFzf4n7XALf0HoT1r2SIcsAXanw5ra8ZIL0Du3HRUohYy3LgwXqPcx84HEFSQBKhkwQxumzoW8Fa32QhJoI8AHAuCxzFhfftAAZIpjMNlx80xKBBwwQ6TqD9kcOOeVrV2IWiTAFelLB7/pzKTiPEsm8JgIjTudHtARJgR3yzAQxAVqbBtVFebNDzml4LXbRGCygNpDtSIavlbaJkVrV117Jh+TVUaIpOzFGZMYjkNMnUdS39SNWROtdCO8ZlcveUOpe7pibxc0z1NzX5X6I938d72cqY1JiBXD0rmYIWS+uas3o1Tm7qNw2MXKx/JQIwmZM+HzCTXkpbyI2E2SjII1OdVhJMJ9A/h7PqJMjBcEDq0fAeoLUc3cVTmZOBOI4MdsPIyajdfuLZPl4ccsz1bz6WALuTTDWhBukNdyaCPcDt5xl0k4G8VehuFUYzFH+LkrzYoLeGYltLJQHpgdRSz10GfokCjPtR27donQi8rL23NYBc0ihan21uMs4tUkp0Za6OlvZbAGwtoMaSA9HUQiBXb2s/alp2TWoaY8s7Uyu35t0oaXA1tiEjpUhGJa2vZa9v/lxatIVtx3t8WjPZtzfApCH2R6DvIDoFu3VRTSRxQmlBsIJgegFYB3iT4RRC1rKF5qTZ8V3zZzuMwDjMAHyWUzSkL0NwS/ntBf1PZkn30Wi4IFOd08TJdvdH0KaDlBJkv1xBL5baBFG7NjdRr6WwWgZ47dr4c05Kgzo3EeSuXdO/O67mT/wJU30paUc5Da6FAryEjmwBmbhs6l1s6U+tjVZadQ39unYhtAZICkBW3A9vAaecNn+X8dcgcaSWVHSWlAvQ6gAQqbnLUZ4nwDcX52zqdpqA4xGkFGjvMhRIB0iCnb6tErx/NmqQ1qDjOIPcHD/TR12xxmVSCLQx4BTcwr9eIu0uBLv52YTJreds6QJzgl2wpmeNBeQIGkcXcPvLGfTi1WxJm0m9RK0Do5X6SWlUd6GM4mtKE0NNy0vdExKTT4sWVZLUvUb1FI1/cvW1UIglTSi1CIo159R1uWM18deUNK5WzS133Zq+lsrmQDrXNtD2Hhfe12qQBa3L73NukZor39rnSplavNqcVrgqVFlKQn8+cZrO7CRep9/3G0agc+nOgwDOgAMbf73WsJ5S9CmOrElnSii5J5z197Rca9Jtn2B33rsUwllzuqw3UsB+2dKbQBnwUpNCaTKPASunfZS0ndS5Fjon16dcHbXBu0JjzAKev3btZF967mvKhedqwFCbtFPPI6dl3pek6KoSHen/tgBWTRtMvce5RVqu3dqC564LuYJkc1iW+tNS5q6aa0JSRiqr43Heg4TBqmGdWb8DQKKJv0t5bhXqn7UHuRSoxYYjLdFXclLY30tmaHDH/P2QlFhiqK2XFx/0gPwLW5pIamAYX5+q/9KB3jL5hVKiXnPHSlpo+D28rqIRNkvqueXa9uVSAFSj6EoTdTyZ1wCjdP4S4E9Jqd3aPaZ+q9J1qfpLgBq2FR8Pr2+R+wLANdpZ3H5YR+0+7qGvrUYuF1lxpvocxew8C/rs99MCmhDAAnAh8IT7bimryJQhSqBZJjW4cJ8vFq/FBXX5OmLgC1Mu+XrtNMEmcn+2yosNetYg6adX0hxyZVu0sVaapJUSbNGGctKq6YV9SfW95d7uWxNqAdfagiT1PQXyJVBIsQSlPpWuv1Rqml3pnkvt54A794xDyjD1/ra8y6W2Uv0rLcpa2y6BmH9+K66/NCB863Xxvt6dIzrF2qSnJwUtWl9wbgaPENy0TgNbbJTi6/f34kOD+QDZMZj5zzVaM6M1noEfsMQiLTEBFXkbOJ0vssQDtzahpiaDeIClBv4aCiSuM9XX1PeUpFb0qc/hBJkDg/j51DTk1PWXSqmOlHYXT84lgIt/sxrYldqN6871OfeMcu/emgXSGo0qdU3u2bTWW3p/WzVMd/3Fof5aqdz4d4/PrdTycsAV30dtHy885kEuB4xNgOmffXyPLr1PLltDCqT4OgOr9fwPwDmlHMTDjOvyx87qPgmMTWx04v91iqlJH31Fyiwg5tIVzfTrpdQqXnRNz0vrSjk1mFs0rdJEHdNGNW0pXlHnJpcaBZOaIFpWyXGfS6vtWC7R9mJNKvxdwv62aseti4C4zfhcrn+l+uLrUr/BpQuD1kl+7fUtmmDuPQDaF0ap3xl5EFmlOZb6GI7pmta8FvxPmsq7KJw2lU5NlMtReaeUXqnfKBG0OtT+TiRyfs+VLSWHPaM2hYCnL0m6SkNwsy7zu7cKBU5o0OJermuPAbhYrCgvPujl6Ln4XFgmR+WEn+N6S1pkro7SxBGDZXhNbnJpAcNcn1JAW2uvRUqTSK7utaDaMpml2i4BXgqE437kgC33PSe5tuPPsaQWVbXrcwux8B7v8nvnJPfM7rPuUHIglutH7d1b0e8Tq9K5+nO/vJyVZ+rcaleGVF/DOkPwSwFeWC4+19iPHOCRN5ZRyiWDdQlhAfAeo+HILtqwgc00zfuJsQtDLNXs7A3yYoNePKED5xNd7SVPTTopLa51IMcDLTf55gZhaQVdG7ilfuTqv+sEVZqQWrXYuJ7awqEVAFLtxQBQ0v5y51N9X6OdpbSsFLi1ammpc6n6Wvu25rcrLTruG/xSkloUpuaA2lhe09dK2ZKhSuseXhUES5q6/x4mqc25f3hJaa4xYAafzxzLQ8DrFKjrgO0Gtvd+gov/HY0aGCfQOMHuDxxC2gOfizJzBqgl4F4pLzboWQOQbH/ZU9e3tJGqz0tuEmhZyddAuUVaqanUsUvqbilfavfS+235/XK/dU6jS0lNM0wdWwssuXpL/c/1o/Su5erNTZK+jlqZuP+p7/cBeKX3L9W33D2ktPgGaU7YjFOQCh3Ua1kXUo7tF0nqnTGZeWGNhMAXUZ5JwJNyBjzz4Ap2I11CWI4EAyKO5zkqYJhA1mWft2ZxSThzXYgAz1jcJdHAiw16sZTALda6Wl+AtYMmpZ2UtL8USLa0E0pNu710NV461wqIl2jIvv6W/tW0dH8sbKMEaKX3Ir7+LmAX9rf0PdW/3OIrd21Yb4p9yC2awvYuAbG7antr3p0USMf1rOzPGivOVp+8GBBz190ZBLmy/KIklNp9JvpwAnj+r6M0selhHl9DX/eYA1FbAESwimAgFwvKQYGUhDWSac9EGzbusiDcJbfQF4F/+CJIPJD9v3iglwZvPAnkJpUYsFIr/bD9lokoPBb3qSSlgV5a5ZfahBvArRpR7Vjc37gPuXLhM8pN0CntNb7X+PMajb82ibbKmsm7xCKk+pJ7pvEYSLVVurbUh7j9tYuAVP9L16fqL7WVei8ukYoWXzJmqYFZeH0qRmfKrSFV/k73kPodw2OhZWhofJOIlek6D9puYN71EIfXrjG8q8d0rWC709/BCg6AbTsJ9B2w6ZeIMd4IpjF6yyXyYoNeitJISQxScfkUJZdaHdb++vJhG61gVpo4auAZf28pWxBr7GUgXCtX0yxyv02u7tT9tmhqrdKihbXUvfa5tWqUtYm/BhbxIq91csyNnZpWkdLeW0B+7QKrpNHH/YklOMaRP+5HM4wDTMfaXAxwuTrWlM90Mr0YLEnCFzA2KCEiQAqgU9C7DqYXMB1BbwTGK7enBwAuEa0VBKMEbCdhlVwsPcXp3zOaM/V5pbzYoJca2PGgzlEboeYXa3DxJJDSmkovS0n7yg3EFPDG/UzVGdZR03zic2vlrlRlSnJaXHg+V24tCJUWA60T7lpt8b6kZXGXOl9aGOXGSlyuBZhapMS01GQt89Dy+1cYl7WAspaKTLk2tOwj3qXNiyVuN9TE/GcSgLUQhwnd8wlisBwsWhLMRsISQBYgf6/aYPY98D59VGhHZD6vvZWLr/xSkdSknlrFxKtafz6WVoDIDaoaOJVWxiWgC++npV+tlN8KuXiAlai20gIhvLZ2fQnoc30K5a6gtRaMcudyC6MU+PtzKcDKPbPUwqikKd+VIrwvqQFmy+9Xe84NddwnyIQaW6vmdhHFuVbCZxNagJpMyLHwOmNAWkMMZk5Oy+cc2BnrEsJa9z243husZNq4L8rzS+SNvoO0aDdhudT1a+i2VH0lja9Efab6kdLaWiiIXJ3xuUtX29bcfZCtAYYcxRf+tqXFQ62tUGsv9W0tNbn2fAt1l3qXY42ttJDJ0ZFxXbn+3GGhdFbPXaRVsy+NsXgxFZZrGBcp37ssEK4cayHtmWo3jtt5JwCu9c2fS8T4PDFiCcOOWcu5+I4Ta3EApq3AtHN9npYEtTS6jOyHI2ic5voZ+ATm+Jxuf+8+9/jeOdabMQj5CS31t3Stl5xGGE84MSilJmf/vaSBlCaeWt/XHM+1WZNWELmrxGAU1x2fT5Vv1fpSv194fUnjWiu1fpTKtC7iWt7ZuL3csfCalsVBi9z1/UiNg9J7lwL00gKiuRtpV4W1UnJXSNGYpVBnNdeIxIVt9x355Z1JKnj0OEHcDlBeexUEeZhAo2Gw86HExomT1U4advKBsAlkwOmEXH2QEj6lUbYfK+SdA3qx1CaYnJQGSWqCLE1YJbBKXZ+rM7cqK9Udt10DxzXa0SVSmlTjCTbV/7ivqf6kJv1LQOpSACwdSy3Acv1rZSlaJH5nc8+3Bhxrfvf7AMhUH3LfU23nypTep8b+rrKcTFybSzab8+9LWXz646VURhdJ+AxyrgphIGkfbmweJ5bB7PYINRnI29OM7DCGE9YCcwxNO2nAaOd/JwBhQM4Rb77bIFQZAcA0XnyL7zzQy63kUpOu/94KYqXJJwWOqXriiT43UaY+5/rQMsBL4Fg6n6uvdC41iaS0V1+2ZeLJgUTqtystJHL15iRVf+661G9e6t/avqQkB/QlDfa+fvOUXLIwql2Te49K707q/cq9g3cA51Z/utjqMle+pM3F7bXk7Mv2reWeA6dway1bswI4cSsQtMTW9HVOEwOTNpzIFmC6c9IcfmzO3OD3Chctj/fzlr4R+QWjSyo8a3z17ufknQd6JbBaC2S5a1rK1TSBXL0tVFTLZF6Su2hsLdK6Ko/L+vLhudTzymlfd5WW37qlTO03frv7ktJ+WzT/lOa39l1Z89vfVXILvdwzKbENoazod07ra00iW9Po4nidKY0vBsXGjufPpSw1fWYE/x1wmRIEx9cM9/kUR2axvu/jBAwjg50LNH0SMdpZbnL/xZJqyFpY6XwEHZBarbns5Tlk3wGgF0+AuR+zlY4Kj9cGSm3lnzuemoDi+4nbyl1bo6VSsnIiujTHWLat0qo7PN+qpedW8ikAzQFm6VnF/Wg5l2qjJiUwb1lUxe3m3pHcc62xBWslp23Fv1WpjRYqs/Qu1a4t9XuFpEDKHy85q5fSDuUyMoT1h8dT4NcMguE9B8GqQ8CLNTtSij/3HawUIG1g+w52owAD3rubszZoYBxxlh5BcHBqUmqJ6uLbNIbrmDQD6ETL9V7rvEBefNAD2ieD+Fxq8ozrC8uSaNMyYuqytmpPTAT8ImcA8O2gpQpyJ6vNGsDVriv9Ljlar1ZvLC0AkqMr71Oba3lv/ecWbSYFbrn3qFULukTi9loWavF4i8vFdbf0txVoL5QU9ZgCrtw1LftzOYC7d3+9EPBCzU4p3sOTctHoOgUIgrmSsJsOphOgyYC8C17HYcdIaw4sHfjmkRRzFgbbd9yWkrDkkuBqNoyhoB92uHw/D3gngF4rXVlbMYYTQk6rq1FvOc2tRqMkzp8BzT3RRmuC6H5RJHcvpecWlonrKj3rsN5cey1yV+2nxEpcQs+VtMwSM5HSnMP2WiR+3vcFli11pvqb0/7uu39nXUmHF4sBKRVe7LR7ZavQXGDrsB8XdP70uYjARcDv37kUQeSzJiifDFbASglIgr7uoXeKndC9pgdAHARbbWq9ZFQAZhBF38FuexedRQBCwCgBMWrQZGA7BToOTuub7uy+8OKDXigtWl7rJJFbgde0lhY6JryuNPnkJrnKoC0BVymBZewLdC/g16rVhWXDvzWNOtXe2mtawC63+Kldk1uUre1ni0aYo19TdeUArrZAqx1bCyiXaJ61hVBu8ZPSJO9JctRkKtJKznLTl/PfU2Ox5bocDdokCcMV1uwEA17fMYXZKUAyFWmVgOkk9E5h2klYBZAGO6A7GxUz9qztceVcr1IMoJsOZtPBXLGWyNkYAHt0GRmIQLZjoxgpAWm+zPf0QmmZjGplapRTjXZrAd5wQJdW1y0AmzheA6vS3kBcztfXDIStk+PSyFImVVeqTAsduUZSWvt91Jurc23/a+9jrf1QUppmTmOKj7dojS1yCeDkFok1DTAHlrX2axrjXE3Zfy78nnNVKNVV+l4yfIk/F+XkfYiMVYSboxydaTvF/7ZsvGIlwUoBvZGYriSmnVjCjWkLebSAFaAdU5fC++Zp7QDTUaJXHfRGQm8EfNog0j7tEGBHAjmL0bsuxe9fz/9SlRIQ1cqFgyaeNHL0TghoKWoqBMA12kPcr8ygrL38a1aAq1aLtUnIS2riTWkqOS03fMb3RTeW+pz7njpee6cu+a1bpNTH3AKtZdGRqj/+LXLX5PqZW9CsKZ/TvnPjqXQ87kPje1VzV4itL8PjcXSVHBCW2o7bCI+Hn5tYG3e/4V6eN16hzml3SgKKtTHTsYZneuECSwuMO8K0I+gOMB3B9JxHT28E9FbBPNgBXWDt2SmYrYLeMmianrU80namRzkBLWCl+IvM6VVJUZrxsRzlGNcRHo9XvbGkKNJWMEjVk/tevPQOK7/Sqj57ycq9wJbnUfttavXUFjo5OjBVPv7tUxNlrs0alVmSGnMQtlliJeJrUuXCz63a+qWUYW3s5AA8NSZK16YAM9eX2rjOSOm9L7kYxHt9593Kg1ncRs4IpsmPkEQ+gLOged8NnVr23YAZ+KwimA1B9wTTw+3pEWi0IG0BAoxLL0Smg3iuWNub8+VxPcfHAmIC+ucaYrKgwUCMzI9y6LKJn4E1p1FgVso7V9NLgZX/XBpw/tr4WK7e3ASYG7xxHbV7yPVrLnJOSaZi+FHJxDelNRU0yHu1FEstPlIr8toqPe53aaKr1dX6uVXuotmV3qMWhmBt26l3vzRmUpRhPD7WtFsCnBx4pzT+lsVRi4Z/oZTGSQxiazS21CI2576wKnKMsbOrgvVhwk7KWAYpYs3NAx+IQcsSwUrAKD4GC8jBQIyuTkUwG6ZB7abnNES+XtdNdbQcpNoGmp62oGECxomd2+e+Xj6m3jmg1zJovISDsTSYU6vN0qo7t8Is9df3J/xbKhtJaiM7UzBdr1/lhQM0/l6geu5s9FL7HeJzueecopzDlXuJG9j+qQAAr85JREFUhiy9O2snwXuaNE+k9T2673Zyz6XUnxAgcxpjS9stYyGlyabK5WjQC59dy3ZBCXRq+3mx0UpOk0tZi5bqzo7R6DmcWUdqF0JsnIDJLKmBDAOUFZipyPkeBdiYZc6fBxjF1KU3gIG1gDYQwwS1n7D5woT+yQS15/ic3m2BjhNof2RXBW3+IvbmLDlevnWvoFY+pkBS53JlU3RhakK+wwTWFHvP1+/LGXsOdqHE381y76s2yUtUWfw8axpYqf6YgizVUdIaam2mJKWx5srk+nhXKb2b8fjILQgupdRby+VAMAdmpd8jRUe2aqW1PhSkxXBlreSsPFP15yw9Y2l2Z4ie2ZxJwVpYbUBkYIfh5HphraMYJ1ihABKQg4VRvKdHmlMHySM/W9PzPqA4shsCtGH3g+MAodjZXRKxJuipUG/lOYywwwCMA89Z2hnCXCjvHNCLpWVCWQOQYflc2ZLGlppYV4BcbHHZBHKptkPx9EgQAeEkdUgg1jqALLxryX29Gr2bWzDUJu+4nlIbtWtiGi9FU9/13VijLZXqv6RMC5VbowdDqS1icn0ogVEMdCWQy0nqd7wrIK6QlFV0q5Xm0sX14Fnz24stRzMNnwMf4MKBGQaZYTwNOC00hFWQDqTUXmDaEDul06LZyf0EmgRMLznbwu2BQczF0iQiCGthpYSEY1ldMlq6PQL7A7srGMthyIw5C+yyRt65oFeSFk2g5brc6r5l8mhdGc/FT1d6xZc4rjd0NgWywAYhMB/1nLmPg7eyj5WC6UVAeK6kofjvqXpa9pFKk2cJIFK/f+6aUr05LWat9pe691q7cXvxta111n6/+HPqWO5eUsdK7aXaTdW5BvBWjk++5HyM1sqdd6XuzpDyAYzbTVGfcR+S/fPlBM1pfWZtjzRITLMWCCLAAEIJF3ZsgpUKVglYAbbY7AXEICCPGur5AHq+h73dM4hZ1vgswIYt2x4QgBw1Z2U4DqCbPeyRAdI6Dc/60GYXyjsP9HLAtGblX6rPX99Cv9SovfC6yoo0u48QTyah9oYA4AJrp7mmGPjO4uKJ+S85LTB+HvMg0xokZRvw5e4/txApgUT4vaXdtdp9S125dyt3Ta5savGUegap63P3lXp2KW027kfpfWzV5u7ye5XYgNRzKN1zro/3pPnltLq1Wl6u7hJFGYcxC8Gw5BMYng8O+EoZVKTfdwssLQHnLG7nBbGQxAlij27uMQrDA3ZfICOhbjXE7QB6fgt7ewCmaaYnrZ34ez9xxBaXfBaHI+ykYccBVps5APUsdzCmW7WU+dmf/Vl83dd9HR49eoRHjx7h9ddfx6/8yq/M5w+HA9544w288sorePDgAT70oQ/h05/+9Ekdn/zkJ/HBD34QV1dXeM973oPv+77vwzTdwb0+lhp1VSufOx9rELnj4bHS6jX8W1mRZl9cX86DXwoY4ySPzgLrJCq6/xcfB+acV7n+zX27hB4KtbrSJBy2GZfLTYz+WOr55tqp3cOlGtmlUgI1/zcHMjmAKQFCrPnlfp9cH3MMR6qftd8mbqMEzKnn0QJmaxdN2e6dGpss1Se2CTIA1OpaFLaVMp5JWXSG31MAetYn9zxmK05jZi0L0wR7OADHgfPmDSPo9ghxc4S8GdC9dcD2M0fsPjuh21tYAmAta2wO8Ow0wfo4nF57Oxxhb25hnz7jf/s9cDzCjkEZr+UBXzxN7yu/8ivxEz/xE/hrf+2vwVqLf/fv/h3+wT/4B/hv/+2/4W/8jb+B7/3e78Uv//Iv4xd+4Rfw+PFjfOQjH8G3fuu34nd+53cAAFprfPCDH8Rrr72G3/3d38WnPvUpfPjDH0bXdfixH/uxi2/i3mSN1hevvO+JIjltLjMQQrAL6IgTytLTkmHAWHc8G7tOyuVFj62xGkj0VdFaWp5Ny+8R1p1rM/W5pFnW+lOagN9uMKxpaLVzITDEwJErE56vUYvh95bfuKZFpr6nAC9XX629VB0t0nBvNU1rXXN1erIEsq3U64kYC+v8CRi7LAOYpnmeIMHZFeCCUIth5KzpTyU2Vz2sIKgnB+BwnAFvNkKJFtPWgSpC+tS6fbyoX3cRsne0/3z55ZfxL/7Fv8C3fdu34d3vfjc+9rGP4du+7dsAAP/jf/wPfM3XfA0+/vGP4/3vfz9+5Vd+BX//7/99/Nmf/RleffVVAMC/+lf/Ct///d+Pz372s+j7vqnNp0+f4vHjx/g79A+hqLvbyjt1bYkKSl1XquOiLlX26eKXPdTOSkAX0qFhXf4lsoZfdE9pAPOLefKaeAdR+MsuGNy1hUKOGm6VHJXZ8nu21rn2+rdDUvTlJWV8uVByi5RcXWvBJlUm7G+ufzVNttTeJaDYunitlMtRlZfSnaWwZrm64+NrnddPUg35jOnuL3939fjEsi5ItB1HYJzmRLBnQBY2F23NnGl31mCyI/5f0/8TT548waNHjypP7VQunpm11vj3//7f4+bmBq+//jo+8YlPYBxHfPM3f/Nc5qu/+qvx3ve+Fx//+McBAB//+MfxtV/7tTPgAcAHPvABPH36FH/8x3+cbet4POLp06cn/05k7WTYcm24il1TxwUT9Spn7+DlIynZ6TykJ6Vcck0J4coIF1GhB23cv+0GdLUDbbegTT+fn/Nm+Zc2pEbDAZSiONfcS+teitesQqotLpfTBsJzud9zzaq/9FuWaNa7SkufUhpoqUyKYkyVy9H68edYCy71paSF+u8xZRlTrqGkqNIYOFt/nxLtW5PGcjVXg7hc+D3ev8vRmWv8BNfMPzMTNI6w4wQ7DGxocjzCHg5MU+73TFU+v+G/hwPsMC6GKCHgeaf40Dk+8MVLAd5dZbUhyx/90R/h9ddfx+FwwIMHD/CLv/iLeN/73oc//MM/RN/3eOmll07Kv/rqq3jzzTcBAG+++eYJ4Pnz/lxOfvzHfxw//MM/nO/UmlV2y0q9VleN8kkNvsIKNOWjM18bikjkuALOQvIQkYtv51ZoQoKUS/bYqdMEjILYZ8ZymB8cB9hxBE0TExtecwxf1FA7nLt6+cZykR5bGqhP6rnvLdfWQDGnJeYm1/uU1KLqknYu1VRrv0VKi27VqGuLyxgAjQaQWdCUFjBvx2LES/T+5liPFgOTmpN6eDyuew3bEju+V90Zgtye1n+cFkvOudXYJgBY51NnIpCLmah7kNWg99f/+l/HH/7hH+LJkyf4D//hP+A7vuM78Nu//dv30pmc/MAP/AA++tGPzt+fPn2Kr/qqr1oKpAZa60AtnStNcrHGUVpJZgZfKsrC/OIl6EzyGYulXF6GaF9vFikZ7KQAug5208FebWCueuitmp1HrSAXWcGCJgN5M4CePIc9DiCaZhNhAgAKoiFkfPYuojprVFV4LtYoamBZa7c2MV8CaJdSqHc978sA6XIl6m7N+156zrk6an1OLVySFCqdH8/9Xm8n0IVCIglW+eLrxkc8puK2alRmqo6cu0N8LDi5PM9gvrHWcrJXLFoZETHQJdydTijLsL+pRZNw9awZ0w2yGvT6vsdf/at/FQDwDd/wDfiDP/gD/Mt/+S/xj/7RP8IwDHjrrbdOtL1Pf/rTeO211wAAr732Gn7/93//pD5v3enLpGSz2WCz2dQ7V5qgWiaLllV/eO6ClWQTteHrijS7Gey8pqYoAAFXl5sUyGchJoLdbWAe7aB3HfROwiiC6TgtiO4JYrJQewN5MLBKQFkLekawB1el1rC00Koz8CVexpxzbOPDgXs4p39zwJaaFFMTcm6x06o5liQHLpfUex8DuwXEc9prbdFQowv99fGxGgimwLh0D3E9KeBe20ZLHwqyFsRSktubK7lC5OJ1pvb7WgGy0PHwgnnRHQOY1wQpALyQprSIwC71GVgAz7d3T8B356WQMQbH4xHf8A3fgK7r8Ju/+ZvzuT/90z/FJz/5Sbz++usAgNdffx1/9Ed/hM985jNzmV//9V/Ho0eP8L73ve+uXVkn8cNuWSGGg/pCWit2Kj0zPU4BnpSgjhMuoutBXef24ja8N7dze3Ndx2VdWB8oCbvtYa630LuOU3e4dB/jlcDtuwX2Lwsc3iVwfCQxPpCYHnQYX76CefwA9OCK9/s6xYDr9w+9eMOY6HmF99Q0GfjnH6/cw7rXPPcUCIb1pqRlAXMXzaH12phBCOXSiTm1AIyBLqlVJbS8FJhcOpbC8zXKM+xnaUGTkxYt+n+xtFCiwLl2FoNgSntr/Z47lhVjz60po725qq1kbTFyz7JK0/uBH/gB/L2/9/fw3ve+F8+ePcPHPvYx/NZv/RZ+9Vd/FY8fP8Z3fud34qMf/ShefvllPHr0CN/93d+N119/He9///sBAN/yLd+C973vffj2b/92/ORP/iTefPNN/OAP/iDeeOONNk0uJbWJLKU212jNlBYRryrfThE0U5kQgjMMS8GgphTslg1PLBHgop3T6MIETWwdBec8aq820Nes4VnJEdJ93iu94cExKYLuOEo6CNB9D72V6N9SEG8JdirlU7COxz+hOe9C55SeZ24CLk3Q8eewXGqCLGmVtf7Ex1IS07E1yfW19o7njpWeS9hmDLYpMIvrr42JnDYYtxOXqS1S4rI1TbJ2PCfxfZXu0x3PvuvRtaUxkdLSSjRkzSAmrrfk/pCjaM80wdxiwx/3e4C1ftUUjDVjp1FWgd5nPvMZfPjDH8anPvUpPH78GF/3dV+HX/3VX8Xf/bt/FwDwUz/1UxBC4EMf+hCOxyM+8IEP4Gd+5mfm66WU+KVf+iV813d9F15//XVcX1/jO77jO/AjP/Ijl99By2SQe4A5WiQGvlT5O0py7w5YAM9bUXYKUIqTOF5tYa63S5bhrYDpCTQB8qAhRwOxnyBuR9BxYNBTHPOOQwIxrTk85KCwpnOpQJbGAcspPqySILNBpy2kNj5bCLz/DFXMjsNBWxyQ8aQcHj+t8PRveG0K5HKgVBpIa37b+y7XWkdKa6uVb5k8Us8XSD/j3DMPy5f6VJKaJp4D2xRLcFeJ62gA06Sjd0JDbqE412hlLdsILW2m9gGrkvvNSuOwRd4GLQ/A3f30/lfImZ9eq5RWjqUB5D+H0qIZnDWTeLkCOhPAqbFKx2CHroN9sMP0risMj3tMVwKmI0wbBi5YoLu1kIOFPFp0zyd0X9gD2kI/3GB81GG65r28aUM4voswXgN6C1hpAQsITeieAf0Tu9R1sOhuJnRfOEA8vYW9uQWGkR1MjeG/wJnf3vKI2gdkUmoaxPJgy1pYaWIs0Ww1uacF0L1J7R5isCg9j9zx3CLxrn32EtfdIm8H4H2JScknLzzfWo//DJxrjl6aQe9/gdzFT+/Fj73ZMuhqZVIUV0v5lZK1jooNVrx2pxSw6WGvdxhfvsLxlZ41tg4wHUH3gJWAlQQf1dxKCzIS8tCBjho+ySMADglEYM3wyE3rHSBGAiyfNx2gewIZAFsApEBmAwVAGBcc1rq/UrK2JwiALIJf06CMJ6zchBieS2kw4eo6PB9/r9F3cTup/oZSWjS1yF0BpLagK63Iw785tiMFeKm+18AnpX3G1+TANfW39M6E7ZX6UQPKhjL3FX0l3XyedmyhN+NycbSn+FwO8JqBMLXAuovWd4/y4oNeC+DF33MTXPg9NbmG30sTcrYr5y4KAE7CgpGUJ4BnHl5jemmL4ys9pp3bN7CAGC0Dn0vfoTcEK3jnTUwCZiMhg5xWVrm2JaD2ABlATAQy7rgAhAZgAb0BLBHU0QElWJvuhgk0cighMi7JY8r8OLrn1j2H4vNL/Sa1a0vg0wJmNWYgd13ue01ayq8FxhxtWaov9z31jpeeQwnQ4vZzz7nlb0pq70uqntIYbtAe3y7Aa2mzZY8wvi6n9a1xmC907Pzzlwgr8uKDXk1qq/kS4OVWyKlB0jgoTqyrSCy+dd4y0ltJdh3sdoPpXTsML3WwkrUvIxmwCAxIVi37cmJk4JMjJ2MUR95zI2shBgNAgDqCFU47tExn6g3B9FyPvWKDFjEC9jnQ3QITAJoU5FUPeejZhcHY2ReHjGEz5LtoefHzzD3/GgjlwCxXT4sG1Hr87ZK3g0ZdC7Dh5xwIto6fFLjchZK8j/q+RCbkSyXnjlAae/G++2qr6xdU3lmgt4ZeatmXiKmW8LpV3cq/SDOt6a00vdHKboPp5WuMjxRreD7Yq12oSvKuL4qB0ErACoKR7HRuiUDagCYLaQ3Ichm9kQDsrNmJyWIEYXwIjA8srATk4IARQP8MsIqgdx3oagsxTnPm4yXvHp1kVg/v23+uDqScVl7TznN0WXi+5dpcn1onxNL7dBfgKoF+izbaCuwtC4ySVhhSyv57SXK0V42izH1eI7mF7CV1rqFJE9IyNlp9+OLjOSvMlCtE+D3HSjXLWpbkiyjvLNArPcwajdMyQNdeMzeT2MsLtTwijpyiFKjvYK93mF6+xvAu3sMDGICMAmDhnMp57836KiyfI2t5b8/jtTaQhwmmZ4d2ScBWLwCpe8IEghgBvbWYXp5AvYF9q+MEkQNB7ZlKHR8qiLEHDRvQNLH7xDBUn9O9bIinFh65SbI2IcbgVHpv7otKvI+BXgP/Wr/uIrVFQYu2nbrGl4v35kptx9rlJVK6bm2dK9ieFmfzXOSVWHL1xIvN8Hwtqksq3ue9ypeARv3OAr1YUiv6+HhqIkldkzqfK1MTV/4k1x2JOT6mebjF+LjHeC2cwQot2pxgY5PpioHNKAY8mpiSJOPLLYCKyUCAAZCshBndyy4APFTQPe8LkibQKGAFhx0jy/+E5rLTlYAYOoh9zy4RA7tFzIkmG3z2kpLbK8r9Fh6oapN+aaFT0/K/2NKiHZYWbbk61rS35jmEC4aadpcrG5fPaXB30cJSch91XNz0ZW4FrVID1dSxHLAVw5LV5G0YU/dlTfrOBj3gHOhaB15u/ydXf7ELGQ3PH5IMdlAK5uEOw0sbDA8FxmuB8Yqgd4Alt88mMe/jeW1O3gLdjYUYPFBZ9qOTBIyukcmADCC1nZu3UrDWuCHIAeifEqwQMJ2AGAF1S5BHCxGApFUCtlewGwd8WsMasxi0JICv+qLGAJQCwPBz6XxOyyvRmiUqcI0Gk2urRdZoh7n6W+7h0rrjMrl2W77H51K/f4qeLmmDOXm7Fq4r5FKrzvu4pmboUhqbX2ruCvfVn3cG6JX44/hzacIsXRuvVC8dJNbw3ldowCIlSAjYTQf9YIPhsWLQe8D+dNMDCyOddaXvrvMWlwOBNLsgyKOdaU6jCGYjQdpCkIHpBMhYiMO0BIjtJNQNwSqCGAXEICAPNGuPam+hbq2rl2lTMRimVJU8v7e7SPz7rN0bAtZrdaUFTsv3Gvi0Sg50W+uv0bX3ueou9S8+nvue6lf8+6cWLZf29z7K3EHW+Kvexe2htO8XGq3kqM0vFXm7+/Rig15tZVnbC0hRnrX9oNS1a/sc+uUBc9Zhu91A7xT0hjBeM+CNDy3Gd2lAWtAgIG8FfLxW0nA+dxak2SBFjuxsTs5Ihf33BGwngMlytuNxiaQihQCeAOIoQUZBDp7qBIR2dRkL0hZSA0IbB6zB4HIUJxkTaHunE+JFjuotmnXLxFiiMVsAITWJt5RvlRqItF5fA8uWPpa05hSQ5cZC7ntMW6aAMNboar9rSf4XUpmxtLz3ax3M114b1rFaWpmOO8rbDcIvNuhZA4judNDXNIUcrRl+L1GhqT6k6moR5+cGwAWHVhxaTLLbgN5ZmI0FaQI0Qd4KyD2bbfo9PgBuH88ue3sTpwkiFzJMjBoGgBg16DhhtgQ1XA5Tx58NU5pWsTuDB1c5+OdhWdOcDOgwwOr6yx+DXc60+iIpLVr855bfsKWd0rGSphb3reVdiutdO9msuddUGzGzUaozNVZy1GV4TYm+XAtmqfIheH4JAV9N7hJOrFT2Ig2yddzUymXOr9Xo/mJPz0uOQlm7Ys7tJ8TnwmPh32IXKz+WlLBKcpxM5SKtCP4njgS5J3YknwAxgUFRWIiB0N0A6gCoA9OQaq85V562IG0gBg0aNeTegrQ+zW81g58rN3FqISsFB6gmBjxx0GwE40CSbo/AOM19J+A8G7J75inn9Gx8wvwDTE9q8bkWmjN1PgeOuQk7PJc7ljq+FnhzjMWlkurzpeNlzUKwpvXFf3MAlqojVb8/FteXWyR9CYHi2+Efl7PmTBRMjw3uWPo3b3lumXdlbcSXv9jTy0ltAgu/p64t1ZuSxgFzYhbsjD4opAilhHGgZ50WJ/es8fl9Oh8qDMTRVNQe6J5bdLcG6tZAjAbiqCEmwxqcSwwL7QDPZUh3nYDPcuzLQkqQC64iRoKRnGBWDBNoZDDFMIK04awPfQc7wOW9ovMM6/Ojq2h2LXTh6cM8PZfTLs5/hPNrQo2m9L6soQ5r7Zbk7aSQLtEAW/pUeo7+uhy41cbkXaSFYn072v0SkFizu0hLSi0oS2WWxpdzq5r74uwvvvigl1pFp36otau5HDWTar/axdPg0lYb9ssjWgxa5uwFzvWgZ+MU9qdjqtNKAAawHSAPDHo+yLQYDeTRMOA5gLJEsybH7dMJ2LGrgQW0AY0Aej4mR47ZKYi4rslwu0TApgesC0GmNejZLaxlLdBqzYAucLKnFzq8rl7J5p5/atIqaUa137KVDcgdLwFm6dqWvqwB2lzbLWDbQm2WFpVx2bUafMtvnaunpVxrfXHdqfKXsBMrZU1w6RLINYFJK3Pl6/FtSRdzt7QwbZQvlkHNiw96qVUkkKYxEhIC0glFF74ErYMzU3/qpfOantVmph1pMhCDhdpbdlGY4FwKAL0lTDsza37qhiAGCzEFbXn6cXL3rASskLPFJwkLmMilIABEMgYYLWwnQWbZ1LO94nJCcCQYY2caVAnBYcl8Dr8GTe/OgXlzdGPuXCip9yEHCiUwjanCGt2ZWxHnqNhL5dLrW2nO1P2E97tGiy5RlmukhQ5NSUu50kKnVOeF91JzSPfHY0vM+FhYrhnwgDN3qhMxngai03Ip/9wV2t6d/AEvkBcf9EqT3ArA89+TL9p9Dc64P8aw1mUt6DhC3hyhdgpWAYDAZAhWsQmmPDLwGbK8z3fgUGFitKDJQkzuhdSsMfoQYggSzVprAU0O/MyiaRoLSAErBGwnYTcSppMwvXdz0IBgFwXSLsPCZJiO3XWQfrVn2ibcVYCXo6TiY7nP+U7kNZn4WKpPpXctp3GuAbhSPWul1k5YpqQVpijf3Phr0QbX/KZr5O3Q+LzcA/NTrr4tVm3sclAqfwltGAbBn0Uux04y0pX62jCekn27ZBuhUV580MsNqlbKIzpf3H+q0WOJc8kXzhoActH2hhFQA+j2iO5ZB9NvYYn94rQAxGDRv0VQN0xNsuO4Rf/cOHrT7edNht0GXIoiviGCVWIGV3SA1cF+otfQANhOwvQKZithOt5f9NFdhN8bNBY0MoUqBM3X+uddS89YHdC5Sa91MskNlJLWsnSuXHeN+iu1X6sz/pz6njtWO1+6Zk17KZBLbQPEYJ0qk3qW/njcvzsCSZO0tnMflOY93VNsJBZre7nQYu7g8jmwNeDvUd8cyxN+n4NR+OtTxmlxO2ulyMBcXu+LD3qxVOjNEwqABEiKJTVOcE1yci5RppUf9wT86PQFAhFw5AR3gggbayEf9AB1mDTvkZH2BihMe6q9gRgMW1ce/b6bA7y5Tw7ABcFKMV/PfnzuHvwLK4g1tw0nmwXAjuiS5vifcpzYilNrkNYQt+BUQ+PoLDbt6WCI7j/8m5XcMy3RgyGtFl+X+p6ahEt9KdGWd5UcnbgGvGvn12hutWsvaT9uL/Ub1+jOtxv87gK2ayjNhnrX+ttdTA+GYOcXys624ATkpAtEMQeXd3OpD3IRRmK6NBRhLKX3KFRqLpB3Huh5SU2GSLxQYWocIOlYnZT7HIB+b+94BJ4CUhvQsAXZHcYHCqbjqCtzl0feu5Pjon35e2FnPQd2kjMtzIlkPT1hcEp/WgtIwrSVHNi6Yw1PDMYBZkSXukFB4wQ6DrB+Tw9pTe9efPNaaM4SXRkfj68tnQ/ryu1TtfQ/t2ptedda21pTdm2dNe02R2nmNMVcX9csKteCYe7d+GLJPWiKKTeg1H6eP15q/wTwhHDf6bwfgpb9eme0FoLfDHyt93qpzO/g5VW8M0AvnIxCyb1E1sAaARLm1HjFGkBXJuZ4YFd+2GTIH2tOQ5E5bc8CoOMR1liIcUJ3nCCvNjBbxRoa0UlKIaYmXTvOncEqyfVaexJ02ieR5e/zf7CCOMRYJzh9UC+gN3xOdjQDqnXtn0RisZb99TRrmWd7eu45rU4xVH6g5e/xsVirS03MKdBM/cap45cAYCgt15X63qKt1aSFEs09j9Q1NQ2tRltnxmxz2ZrclcZsLVu790w9rXt08X5e1R+PxLl2JyV/lm7ecJ/Ja3qhxqc1B6SYJiaMrJ2pzxOtL3wmseZ3XyzJHQD1nQF68So0t38QDTRrLGCmMi2Qo2MaH3oxmrmxTF2SW0VZ6yKHDewGME2QtweIbQ/0HVtMCgEowaHFvPalAZCdA0h7AxXjKE0rcEI/zFQpWOPTUgAC0L2AlRzMetoSxEgQE+fdowkMnEKwoY12rhBan7zERLREaklo2PdioZX6vVNluMHzvzmtpQRmObCM6yj1eY20ttVCJ6bqq0lqUZA6Hz+j+FyqjtQiJNV+i6ZX6mNKLqFMc+9b7foLQfqSiCunTZT38DzIkU9YrdSc2gyd4kAZntFRcp5T6DBykPnjADsMvCC2Zh7vJy5LwGLteZ9yD4D5zgC93ID0n3MvqftetIBqXb2ulaAO6/bCAPC+HADCNK+kyFoGu04CAk7bY9XOdAKA11QC6ypimpJfWLBGSMRFCRyxxdugSK/1cbQXowim42usIKfACYiJXxeaLIQB6AB+4U1gDBMZtoTa3r2FHWuZDGNQK70jYd3x5xxY5tq+j1VsSz0tIJYaBzUKt7WuXB9aNNLwN6z9fql+pMbifWh8tXGdutf54z283xdIsztCpOGRlEDHCaux28L2HaAkzFbBbBRHZdoIWCXmFGfbPx/RPR0gn+xBz2/ZAM8anqucccssuf29HHuSKvM2yTsD9IA0RZUadBlqpfjCtlBq0Y/XPAj8ywGc0oNEgGEnPKY9Ja+6SIFooS7Jss/cHBcTYMvLjqB3Arpjh3e/JyhGB3aC6/X0phW8QvOJaX2aIg5YDUxEkDsBSwpyMJA3A8ffnKb0fl74vBueSfZc6vdqmZxisEppfqXJOzfwchTfXbSqS+SStmr0ZK6+Elim7nut9pT63iI1bXFtPf4zcFpvA7tz8u6+HYvkQIpWmacFT78GlCY6BdpuYa+2sNdb6KsOppfQWwm9YeO14QEvfvWGoG4tSCvonUR31aH/rAQ9vYHd790c5try20W+b/PcFj2PGvilmLt7kncG6NVoptyE1jqJpq6Jr4uubwI8f72jOQH3Ylq7aH/a+chNGjgOjj5w1lTWOZsrwAgB2zFQeXcD3QvongFPEAeUNh1TlgCcxSpgSSzhzsDWoUJb6M5Zjrrumo7Q3Viom5Hjb07OM96B9YnlZvTMY3qzJdFl6rlmj+XOlzS90u/fsvrMAc/bDXipPtzXNWs1wXBSSo3DeCIrLRpaJDUxrqU+cxPsfQFVyyK5qZp8NoVV0VbC80QgxRqefXCF6eVrTNcKeisx7QjjtZhj/07XhONLlsMdPuMF8v5lgcO7JK62ErtPWpdIemL/4LmJtAV3cGP+RuIbO/+eXaSeB8FolXcG6LVQKilZO2Hc08t89qKG5r5enPsBGQBGw44j8+59B2gGOFbVnAWL4HqM4KSwplu4da/BCZeBwUq+Vhg4QxUDQw40DfsHWutA0GAGSTIW6maCfHoA7Y/squCfobfgcjx//VHegQ5aMyGtnRDja0oLqZQWWdPAvhha4BdDaoxKKLFGVtIkL/m9Wvua+36fbd2jtOzXrbJHCI1SlILdKIyPOowPJIYHAnrLWp3eAkYCx5ct9EMN/LnkfXzif7oHhocCm+sN5BPFNCcMz0VCnPvxhRJSnrmtgtL4uIff48UHvdzDKu295AZp7iW/p5c/GxYojJnnQnmRlAvw+YDOwwhvVUWSeI/PstUnLNdjNmzMYhRh2vB+HhnMe3g+eDVpO6twZOGyOnj3BMAapjnlyJnTrWQNkbQBhhFwvnl2dKmKvAhiw5rCM1jxwMoaWql8anUfyqWgWdPoErRu9vrctXeVUj0l0KnRnvG5FIjFoF8DwFwbpTJr34tU32rXXjreG+eK5kWfqy92U2jOUBA5npNcrLtBBCsIwwOB8SFh2gKmB/QGML2F6Z11OAG642u6W3aRUrfuWTprcSKaY/0mZaY548V9xo7C/w1/s1ZgrMiLD3prKc1cudz+hP+cu27NKhXnNN/JIIn399yqCUTANMGS840DO7Eb96J5ADNSwGdiMBKYDVgsZ10Xk4UYmOZEZtBxwlje5xOju55OKVEaJ9hpmi1OZy3P30NCwsF4r0lkS9eW9gnW7LuUJt+4XHyuBiRvBy3awm7Ei73W9tf0Ofe8ayAT04253zF3fSgtINTyTrWO8cb3c80YaNXuaj55y3FnjTlxJCejHNB1/q+F3vIKWewFxJHHv1EuwP3BonvGmVfYUlzyBKPjZiJt72R+s+cxPMPjKW0QWH7POyghLz7oAXXKpDaYL1lh1wZhg5yszOb2eb/O+uwIggNhk1BMcx4HttxUEqTcgBC00JBkYXpiNwPDAEfWf3Z59lxAaq/1GQmOwuJA0ientcTvMmdqcOcs5hRFtZBjuXu+WC7RuFvAJzyfO9e6iIrPraHXvxjUZ00TzfWldv8ljS+17RCWK4FhXD4lpfeiRKGV6m3pV2sf7ij35u6TiKdJbrxbyVqeFXbO5QkJiANBHQAQZhbIdO6zErCdKgapPrHolFHoMiBt5Rkei//+Bb0ZSWplmaIySwM6HiQ1TfCOgMd/g/ZN9FL48towG2nY0pIOA2AthN7AdgZQAtS77AjWQgxiATHnxM4Z1TnuJhm29rSKX17d07zXxw3yYJADB7NmSkNDPLllAxZjZ4f0kpZ3rzmyas+8dj4HgKnjKW0t9z619LlF3g7Au6s2eZd7zC00YvqytnpPUa8ti841wFViBVrG+gXzQUuKoNUStp8adyHj4rZB5MhB7KlnBsl2vMi1mq24xcALYEsMhqYjTDsJMUgIIUBCwJJYmCn/N9U9Hy0qBr+Y8jTRnHKPIc5ebNALB0rLajwFbqnBmioX/w3bLLzopZc4G4w6rM+/PNbCGsOc/OCuNQY0afaxkQLUSYhOQvSKV2GKFtBz2dQB9904VwcIkAbU0UL33tmdQdLv55G26G4ndH9+C7rZw44j05sp6iIxiHPfWyPKRxWkHmQdmOKJPzfBxW3kFkph+VS7qTI1KYHHpYCYojPXXNdaNvWc4rZLzycFULnFyCUSj9WU1pfqQ6p83P9amYyk3v9U+q3mRWPtOYWGLM7FibR1sXx5337aASQB6gCztbNmJ48Auy8Rpg1gpATZHuqtfaIftMTrDNvzEhi6JA1eUnt+4dwiHAJfKC826AFlGsVLagD5a1MrzviaknZXmjABtEzoyWDUsRuDpzqtDcIAueDPk2a688h0A3WaUwR1YtnI1pY5eADzJvZoYLaKA1cLcGoh4bKlu/0/eTCQ+wnq6QH05Dns8ch7ebGW17AKKw3yO0lJo0+VTV2Xm7BbJTfxr61jzXEgzT6sreOSPqXKlWjMFs05VU/8G9Y0+RYNMJa7UGZ3pNtSEZvunHcy6lMY+IIHurfG5vi98mih9py3U/QABrbgpEGw68IVIAa3r28cvakIZCU2n9tAPpXnWmUAsCfanTEMiNbOrlBnIcxO6glo0PD7HeTFBj1rANGdfvdSojJL3H5q5ZajNVNlV65Gz17qxPX+ZWB60/XDhf9ia08DmgRABBoVSEnYTrEGKDA7rZPW7O/ns7R3inl2JaC3rPEJl8iWNKcsUs+OEM+PrOEdDmxQE1tsAstLmZkkv2jRKlKTpD+eA7Qc8MXHSuxAqr5Uv0oAdQlY1qjLS+jJ0vfWfueeVU3j8n9T4ynUGEsL0NJ9hpJrp5XSjK87a+5yqrIpAWxujgtjcAYa1QxEhi3EMU6Qzwf0SsAKBb1ldsgnsIaAozMt9I79FeTRMr15DZAGjBKQ3jKUiK3N46DVfuEdzBHWx+v1bg44Bb8zDfC+tkjwooMekF5Z5laJ4TUlQEsN8NTgqmkSDZKi++b+BaubWdsDZuMWWE7yCqFhJ8GruElzeSFAfYeTzWsHkNywGwy94lWXtpCDAU0uP99RQxwmiNsj6Olz9skbRtduZLHpn2lCUpHgc8/hzsAY//4pzeKSOlu1v9IE3wIiOS3xLgB5iRZY0lZTlGNuDKYWhOH3+FgJaHKL0dT51PfUvZUAr1R/3OeMrH2fi3F6Y4nbJZEFhhPA44Z47B8GCCHQAYDdwgoFSy6P5uT6QICVwPCSBV0D6pbQPwO6Zxa7z2vIZ8dljx9gX2IpMSedVhzbE1LMBnDwi+9xWOL0RnuBSa0vdne4UF580KtJbgWXolFSAzr+fkc6g6tLZF5A9KKHbQdUJ4DFj09r5xsjQGSX/gnnM+MBMFw1hUBqLdB3EJNC93xialMbiP0EcRxB+yNwOMIOAzBOecArDO7cZr2/91rZZgknspxmfolcQgvm3rGwzniBVWqv9VitP2vOrwHgUp1rn3sL61K7LvW9tABaoy22lrlA4vRA1fYF4SRbC3ACDCdaXpjJRYp5i4KOA4QgyF5B7SXUDtBbQB58eEK26jSdBXoGwO454fozGlf/cw9xs4d18wybe7ILAykJbHrYTQ+75WD5NGq3j6g5uMWeQN71iWhOV0SBEcwlFuI1eeeCXo72KX1eMxGF16zuWlr7SYYWirU+8EvrI5pzGQsrpQsFxH0NKQYLnHPjAOw0gYggtWEnU2OYKh0nBrtxZDozpCJiq6zGfbzwfuNnkCq3SlK/QzyRtU7eNYqwNOmn3p/couoSMPXXtvQzlDX9TR1PLRZT9da019JiJPW5psmFzyN1zzUwy7VfkHm83tMC2NfJXajkw4vowvDzPBfI0zGUzIbuLLw92NBxhBgmyIOCGAXkAegEYQTBbCxoAoTlnJ7dc0L/zKJ/MkHcHDlQhf8tPOD1HQPebgOz66C3HOYMYAtyeZggOgnqFOj2wBFdjF7SFQVO7sX9vgvlxQa9FEXZskJtXfnmqM8L6LJSHL3489kgCNsUi9Z2kp0hzAvoV3SQAPT5CxNmQNbPgP1+qd9o7keQGHY2XAHONcaG+07da+o+m7W82oRWmphLE31cRyy196Z2Ltf2WmmhKVuAdY0WlwO+0nUp8IzbbgWeFvoxPLZWY2sEz/kdfRu0vWI+vIi+PPF/K4ljhADMi1Yf5cm6YwRA7EfIXQd1VJi2nESaUYggRoAGoH8CdM8t+mds3Ma5NE/nHVIczNruNtAPNzCdhN5JjA+k8/u1EKOE2inImw5i00Pc7IH9gecBrTmjjE9bhmXOISnuBfxebNCzBiDZPthzYBjWN9cbAeodV3Z3zZF1pvGdUBpm5sOttUx9gjW5lEPqrLE5NwiEvHpYJqwfCcArPJNYc6sB253cFlITXorqDMuedyDfXmrSTvVpLchcIq19uKSt2jve+uziPpQWIKlyJYlBbc21l0hrvQ3zQ846s8pyBPWeaG414LNOo/NlU8Zn08QL5NsDVCehrhXkjqBH3r8zHWAFG7bIvUX3nEOQicPE7lJz0liac/PZTQ+z62A6CdMLl7GB74EDZHDuTrWRUBsFpQQEEWPsOJ4AMoDZgO++tL0XG/RCCmQNMJUooBSlkpo4S3sWieM1WiSVCblYr98T9HNACFjh59liyyB2GrV+b3Ac083lNpOXTp9/ds8p5Z+Xom/uReKFSe33aalrDQXZwix4aa07tRirtZtjMlLPJNeP3H2sWUzGWlmKrszda+r6kly6KF1bvsQmpM7Ph0/371PHs4AXsDuzMUpEV87bGKH49lIMj7vOasN2AHCa4HGAeErYKAErryC0xHhwUVoIECOgDhbd3kAdNGhwmp6nSoUEKQW77WEebDA96KE3woEeh0r0gfBJA2wgI6A3HfRGoCeC9Fab4wgyI2/Z+G0Vf6/z808/shZ5sUEPaBu04fH4e2mlWAO01kGM+iSfGhjh9/NwZU5cniorMG9in7zsPpux5/DjdhPHshJaT4XO6LVnPh+uUzKrwT9uN/5dS5R2adJPXZeb+GvH1mpdawA3JTnmYw0jAgBGY/Z3KV3bcq5UJrUoiI+XJLeoKIHnXTXDFdfH73LTOx7QmWEePJIJoI3HlQTHyAX43o0LGShO+2ytBVkDaMBaDm8oPw9shYAYevS9AIgjsFgiqL2B2k9QT5wL03Fwz9b7GBPQKehdh/GBwvGxmNOVAZi1RtLAtOVoMGTYKV6OPUjvQFKADpK1z2G8C7Zl5cUGPU9vhhIPgEtB8aQNUR9Y1X62D+TUYMiuCn2/fZLG8DoHSrPlZwxavgwS51OS2EB3nTrtT8OEHe9fxJNAc1LfWBvPUZ85TaQmdwGvWp0twFPT3nISakC5Omt9nPM2RppeSfNr1WBTDE2KTcnVX6NhG7SwqlzCFJxVUZ6226OtOG1PSpzQmlKChEBMXZ6k+bK8V5bSAEONz04TaH+A/Bxhe7vhiE1EMFuGCTpqdmG6PcAenKuCIO6PIECKOSiG3hKGR6zV9c9dhpdot8UocMB8Axzf1QEGkJ0LbeYN6ELjuYCpat3STMmLDXpeciv2HIDVVqmt9EqKwkoNyhzVdVJV44Sfk9RkkwLCGBRDSTi1xp/P5IxaqUxGobjnUtrTSK6Ga79LblJOAUF4PPfdH0vdwxpJvQc1JqJE/ZWO5/qZ02BLfS3Vl+tDrp+pesJnkQLBFEVa0vZLUgLTuO4LAK/VIKu6hxdSmsCs6fkksDP4STHTnj6aEnyweuMMV3yyZzDwzePZ79ULwVGerDMUeWZA+wNvfRBBeO1SG8CHINTudzICkOwXDCm5LgBGcn+MAsyeDVjkwEDHAasxO8EbRRiuBYAOXSfQGUAOI9+HNYu29xd7eoGEgyS3+mzdUygBZU7jy9XVcnyuqu6zVjRnrrV3gTtAyKGf+Pz4PgeRE06AMdeWiZ6d+61yLg3ZCaQGQOHv1UJpxlJ6h+4irYCZW3iVALBFo6sBYSy5Z5VaHKb6netLjjIujaUcIJW0+9xYzt1rrq0VUgq+UDp/0u6Z5SYxrakUuwMIwRaSwhuPSFjnqmCJOM6uB7VJc3B6n+/OgRUZLFaSczYXw6ApLQPNpB3FHYjXHmemyABg4LWKtTxLBDlYqFsL0wPTDpADR3qxCrNlqOkIprOO8iSuxwJC9xDH7ZzCjADuTyWYdau82KCXevlzq9PWia400MN2wmPVbt5DtBH45k8tv5LAtwLwsqbP4vze4pI+9RHNlAOd+QmdhRK6qxXrSQcKC5DUxFqa9OO67kNSGmRN1rZdohhroJU6nqozN64ufU65/rX8Pi2/U6wxxnW0yh0XOTnjreS4jQDvZB+P2N+WvFbnwgxCCg4s3yuYTnKC52CMkQUwGQjnD0dHgsXo8t7FYGYiXz53PqBFff98FJWTuYMItu9gO8m5OF3+zWnHcTzlng1h5JFBz0hAkIVRBNsB6Fjr0z1h2imIBxuow8B+gIHLFPdBFBNV1+TFBj1rwC7+K1f+OZopnBhLq9hUPwoDpBXwwsGR0/KSVl/xwE6AXNJJNdmJxeH97HsczqhUpytLkVP7sr9YnrxWJ529RMMO34MK/XxSJr629L1Fsyy1m6MMU/ToJZRkKDnmokRXlu4hNc5qIJwCrNzipnXRk+rbXaQRQOPtiqq2lwM86UN5Sbdvphj4lIDZKJhecgxdsJZF1s5/abKcdkwICB9kQud/Q6vZSRyza589BcRRLxag1oIQ7TNaTn1mJeYMDcZFJSMNwLq9PIElb6fEnNMP5K5VAnbTgW4F7IhzDa82jxXkxQY9LzVqJ54QchNDyySYm3ha9gqqt5GnRrLhysI2EoB35tcTvSxn+a1iiVeAYZ1SLiu/iHIgKYG+A7TbU3BOsuT9bU4MaPITYhXwcmDQslgB8uVrk3Xqe4puvA9tKPwbf46P1ejE1LlUu/H31PMoUZNxXSlQqpWLF6qp61rGXKvG1zJWC2Uu2s/LaXgBpTkbq4gA8DoJs2UNz3QCnoYxytXj16yKQUgCsMOGAREABpzul4XO4PMeYCChlbeP5OLTBwX7iGI0MBvrrD0tTAeIDUHdWoCA8QGDGmkHcvNzdSDpcnr6RNdwWu5sRZyYj9bKOwP0gPPBnprMwrI1za8V/HLfa8eTRdOmzDlH7zNJRWzwL2YY/Tzsm6NDQvPmE5HyBBDPIqgDbP0V58/yZZRwK0fNfjduBZpMSxRNzsV9vRrYtVB7JTot9Tm+vgYmJWCsSQ6MShN2TkpUZtyvFHD74/GxVL2l8ZS6NrdIjfua00Ljz6l7qS0Yau3k2ozk4liyyT081u5IKQY+T2l2ih2/ewW9UzA9p/+Z0csrXO4/InCCVytBVxuOt6k1rBSAB5az0IKFdykAnFnTC8es294Qk4WYCGoPyAMD3rTl9EWkuZse9GgCpz6bWOMTg4EYNOjIxiwnbXvG6WyzpV1efNCraXAtgyo8XlpFl1aQpesbpWS9GQJAcqWIvFY3+/aQYGsvio4Dix9fIuoKIQA5b6IcAGbSOVYEACu5LEXhzciUoyw0+zO1TL6psq0UZAtLkGrfX5u7bs27Eveh9J6G7eQWeCn2I6clxnXF/Ynvq7YQSLWbqjv828qi1PrbAmyl+ldIMaZuKG68zFqet9SUEthtYXcbMO1HsJsO04MO005CbwWmDQHOClKMzoDFOmDR7ANHFkyFbhXk2LNT+cSaG7mQhcu4DwAsMlw7Oe8dx31+Pne9UQLTlcS0E84ZHRADO7lDsJP77LvnqhIjW3bK0XLi6snl/nSGLPNv6IJmM/hd/ru82KCXoxdLVFTLSj/VRvw5/F4amHeUUubxGXi8BGA37wUI4mgJ0p2LTZ19VdY6ay3jzJy1ez4C4f7evPIMr/f7BB40Qy3Thz/SGtYYYKIlzBC5zyHdaU4n06bVco768sdywJb7/VNlaxN+qZ7g3eD7aXxPChM2ecs9wD3bzOKrVQNs1c5yYNuyeIjrSQFfqs5WSWl54efUudS9pD7H9TdKNvJKuIg8uSCgNR9eQz+8grnq2Py/Ey6XncS4I+gNQfeuGs2ZEcTEwCF9mwbM5EyGfXadewEpyenIQouQRHDn2G1pBmY/zo1lZ3iXn48shxjTPXB8iTA+ALpnnJldHi10z24MDNLulu2i9cECYjRswDK5vJ3O9SKcW5pjjybkxQY9axYtN7eKL624U2VqK9m3AdhSsibjwOk+gFzALvTp6TtYJRfrL0kLOPl63OqKJt57w3FYNEC/qb7pZ5rFSgkIXlFinDg7A8DUybwZ7wbIpPn8ceAwQ2BPVQsGXBtbiUXP4gz4ar9DiXqMJfc75xZHLRN7WM/JoRVWvCkgcn+tsc6pOKoz7nP8jpdYjtTnUrm4jzXgK42fmtaWOt4wFknKdc+8WFlDexHIrc4e4tmZTQ/98Ar6uoPZSAaSHVOZRnE4r2nHdKE3CqErgAxh85YFaQM5MdjJg4YY3H6dlLDSbTkIAiwx7vnxakzRPQnAaTgzazmqirWgYYS8GaD2HSaXVcEoBjQrAnAjnjPExIYunpolDai9gbgdORrLpM8CWs8L+i9b0Ctx/P57jhLK7Qek6gmvr+0t3JMUffECDW/R6NzL0KlZoyMhGOw6BfQdzKaD2SoGPEmcisjRJmQtaHRc+jCBDiOIiIHKv2SKs7HbXQ/bK7aw8lZiR8WgKcDnHL1piekXMpaD1N4cOJeWS0rrtUDOuuzuLzJHXj1ppX7rGt2ZA8kcXVaSFiqyVTLXkuD8Yz679ln0/1Q/a/eS05Tj7yUtupU+jp/LWtYllsz4vNcYrw2/ZSprSotfLXm6kAs6K00BvZUwG9bwrGCnbxNunxPgM5zrDYMIWYI6EsRIoIEBhizjGwQWgDvpuD2hNkMDt9Tn+Rr/XKYJOHKaIvV8RO+AWR4wt20kU5mwrOmRWTQ+OVh0twbdsxHi5gAch3MfwaBfd5EXG/SswZnLQgqY4vPh9SU6I5QUhXqP+wIz7++DQOf6ACyAF5gKhxmLSQrWyPqOwW6rYLadC/4qOQCsp8mdtZdwXLoYDeRBQYYBbScHst4ptpMwvXTOsQRDBFICZDt+uXuOrm6dJRYsYHqCutHY+FuB01bsYkE27/FFk2SzQUBufyj+m9OG4medYwFar8lIMcpM5h0+2Rdy7weJCqCspWNTAJXSGEvUYYu2l+tTaay0aPa5cvfF0FxAbXLzCZYi7pMxgYuCY20sYDqBaSt4P4+wGH8YuIwFfJwXlqwtwTDA+KgnEASjBG9hjFi0JE9PBhIHmkhGY/LuA0LMlpwAJ7Sm2wM6Ite3HmZDmLYCx0ccoUUOdgY7n3FBaEAOBpu3Rqi39hzmbBxnH8GYbiWlcBLUc6W82KCX2gcIJbX6LA3ssI6aRtc6ABrLzTRI46CfVfxZ3ReYMxY77c7uega7ncJ4pWB6cs6fArC8OrQSkEe2tJKjhRgciGoF4Xh0cvtyVknW4jqmSEwvOGisBchZbZlOODqD5tWpJQZXKwlkduiIOJWIM58mczwPLBv8Rs3ZGUqLm1z9qckynNzXUJwl6q8mKXA+qTqiMMPzUoKg088mtRDI9T+uuwaaNa2ulSZtBbRUH1Pfa9d/ESVpiJXqb6x5CQGjBEBggxBFc7Bmb6wiBizsCPFntefxzN+Xxe3suxd3UNAJs5KKwnR2zmuEPpKLZSDDNMEejiBjoSxTq2anoDYSZBSmLVyYNMYsMoA6GMi9gTwaDmT9/JYDWRu7AF5CA73Lb/pig56X1EDNSW4AlbS4Eu15T9IUYSX25QkpTa/hSblQkJsOZqMwbdnSS/dsTaU7wCoeTGrPURFAFmSXqOemk8C2gwDYIXXSrOUp4VKCcDQIT3HwKpTmASom64CV6xQaGB4IjLsO243A7n9q0DCydjfJU3olE7mlCHi5STmc8FPaWnx9WL6m2eWkoPH5hQ1JbtNqnXyvUs75p/nXAmBJGcbk6Mmwj3H5GuVZKl8CutoYaxlfd9ECW+q5QBtM7d+dVpnxrW0Aed4X5/FjBeZ/sAAMjy8rCHayAIhTAEnAev8368ARXAeMWzR2flEVGcClUo9FYQaT4o1fiFGXjkdmqywb04iDguwVxNjj8HLHGRWOBuS0O3nUkE8HiOPImRt8IGtv4BakFrpTlOlAXmzQC+nN2qDIUVqpa2JqqLbyXbPqTHbvdGCc7AGEFNeJU7g4d15VCnbjKM2rHmYjYZRg0+YdLaC34Q1wMQLygDlkEGmmG3g/DqzR+ZBDSydglXNHsC56uraOYvHaHK9IAQs5MPiBLKatxP4r2JS5f2sD2g+gSc/m2tZFcInplGZ6M0UPttBprRrQJVJ4z6z3TSxIao+IvKGScQZA8SZo6p5q91/TpnIAV6J9S9ek2qi1n5O7LkovoT+tQWgxm3o/s7FyS/31rj5C8DgkwLMyYgTU0c50phU+fiWgHegBgDYEPbh4l4pgIHgrz8fW9ONLusVmGKElMGaZ76MUqtCDpPe9BUCYYA8H0DSBug7oFGi6mq1Pu+cTxH6aDVZoGNk94XicrcDnlGdRRBjrMr3fRV5s0Isltxfh/5YomhwdlqPAUt9zx+J6kqeXgXEWXix2SwgAL3QhsJ2C3W5gHvSYrjroHb9k4479eaYdR0PQO9bM5IHpBQ4+CwjtwhYB0J0AKQDWQmqLIC4RYJxZMRxAgukKI7heAmuSgKNjjIXuBRuK7YBDRxhe6iGf9fzCh3sM8bN0v0UV+HI0Zfz8WzSTu0rqPXTtey3tTgYWwlnlWusm38z+YNx+LPHYSF0TnksBZInSzIFhrkwJPFP3Fl5T6lvqnktlWqRyTSpw+hmT47V0QTyxd11UCWt5044wXRH6p2wpSXDjdwQmw4Yt3grSKIA6l81AEXQvIGEADdBo2UgttIo0dvHViyUV+SROVh2WCYHPuGwM0wSamDHagDVCcXNgA7mRz3NEJ73s4YX+wrl+3EFefNBLre6B9ISX09hyA6x1n6Bl4GTOt5o1n8XjU4pXUZueXRE61vL0rsP0sMN4LedEjUYtIGQVAAt2Bj2ypiYHy24HAK8gCbCdgNB2jpru9/XguHoyvIrkOHvBX0kwgsFOTIAwhqkPgDewRzcgNwS4fF0gcba3EFOcVSd1/zv4Z92i2ZUoy9z71EJzVsq0AF6xjPdzvCRSTa586r3PPb9QUmOnBrol7TCsJ6cRlhia+J5KdGapzxcAYS7AtD931k8gCsdnEBqXWLXs5U07ACDIgdDdGpd9nA1DrOKVpo9padxY9+PeTgQjBUgYdgifHKg4x++koUocc9d/9gDk54NYAuCDW/Bal5JI3O55+2WaFk3Oa5kxnZmKCiOC3+cOi9UXH/RyNKN/4UsDN0fTpAZ9KwBWJBVirCg+UgOA2XG160GbniM1dApm08FuJEwnoTcC47XEeEWYtjRTHnMAWMUb4PLIm8+6IzZkGQxra94wxWl8MOC9AU9zHjWkttC7DpbYqsw48NO9B0ZXh1mCz4KYmrl6k/cQxdE6d4YI7L1FmLHrX+zab1T6feNytcmyJLUyFToutRA6C0uXM1xZ28cUqIffS5pxTbtKfY6f7SXUZA2cWurKAe2lmh+W36jqotByz86FiJgvhN4A4wMLMRLUgceZHAy6G+HAzlEsE1w0liBKi2GrbA7tNTAoaeO0quAZhG4TQF7Divf/PDDOLgzLHpz14DqBXZTmuuncUCWICnO2n+jbm+eML1fQq2lvocSrv7CO0kt4D4PhtBuZ1V+uXS9uHw+SLTTt1RbmeguzY788vWE3Aeb4HRBJdhXwfj3G0R5qz5qe2luoozM4UYIHmbaQo+HPhsMB+Rh45EIXWaEgRt6Ls95PTMBpfAyk1kVUB5bjpC26W4CsgRzMvNF+ttoNn0FA3zVpe7lnmJu042O5z/chNW0LOHdHmDVefVLeaqTfceD0vuI2c+9zjhaOr43byIFHqq6G+8+2G/chHvu560va3qWAme1m2ZilJNbaZTxYAxoniMk40PK+bLxAnZ3RNbM06uB8XMktaA88rtXBQB6MAz0NcTuADgPTjoYjLs3A4t2kOrUwCHGYQf8spWDAOklFZE41Qi9eo4spVP86h2UjK83TDO96sSK/YzLZFxv0rIFz+IqOJaip1IoTOP2b26uI6Z+3W/xkFx+WAhDOJeFqA3PVQW/4ZfXuB7on6G6hOgD2g/GUh5iA8O2zhMXKi+Di3gVO6ke2pPLRViiIs0eCuJsEWOn27FyYIRDNe4WLLyBrfzDOtyikTrx4TQ+Yf5s7ZZVPTfotlFvumpLcRx1hODbgNCIFkH5/YzrQH6+1W1sAlI6XtOTS8VyZGqtSWqiW7rtGc6YkNYc0XZZelDUlfQYWDUcbYJwgDhPkkbU5K9we/NHOGcnJLTTFaKEOgJkAeeQYlupgoW40MziTgdiPoP1iGRlS43OACy8SDGxzgfTvQT5aE1eyaG0l8PMShjAE0uHOfN+Cv8s1lyfUe7FBL7XajI+VVvthmZB2qQHgJZRMRc5WhKkgtORi5m166F0HvZWYdnL2fZkfi6NE2HfGztRHGOzVKE7zoUfeh+tuNMQAt1/nXsCJV5wL3eiOewCUkq3CLMfb434Q/IY6uy8AnA0Zs9GLHCyEthwRJrdqE3QSh7PJT68kpf2hUrk12l6pbKq+1KTqwd1FWzmTFOCVwDul/aWAIPVOh2OiRZOqPavcGGqhTOO+5YAxV3eOgs31M9dGSoL6cpkWkr+lNYshixeX4seO4xzdZKMIYpLQvYtqAo7MEsexVAe2lpYHi+75BHlg4KRRc4STcXIxdYOtgzkLC9OGc9hCH0IwADCrHAvh5wIfqnDkiYWA07258J5iScT5TH33x+IQaDYG0RXyYoOeNTiJyBIPmNTLG1M/LRRH7vsd9yaymRPCMqGJrg8y3CmOsuBMgI0iCO2iqmgL7YCHkzjS7LfjA7xCAHrLGY1hAP0UIC1Ak4UYgDlArX8BdfB50gDxKk90CtYIGEGQg8FkXNQHcJs+lQgAt08IdDc8OEVIb873J4BxxL1KDuBaNJeURngXaWnDZrTaEh0Z15d6J0sUbgiCJfqypIHVAL9WJm6/pMHWADg+lupLi7Rqepky7RpeQCE6+o60Bh0GyNsOnfPZ053EtFsYlPEBMeA9YyrTa3fdswF0HHkM+5i4PmOBCbQ8zyiFgeT7DnbDGdAhxBKjN2ZltIUYJqDvQIcBGMc5Dx9pzdRnBpiKfn9BmdRfImfpegd948UGPS+llWZupeuPpSa/VuDKgWnl+ia6LrnK5xdwtpYkOGMRgYn903kQTBZyYIdV778jD5g1OEsMRh6QzK3fEwgMS+LMCbGTKBHTJZJ9gKQgqI2A6SSmHe/r6R0wbQG2LLOsfVoCPgfM6UiMWQZhGNrovqVlwk2Vuy8NptRm2M3YWT2nabXStuH33GJtLd245pmkpATgl7AnOYBsPR+XqWm3CUnRmqv2n2OZJtjbPUgKnqSFNzLjKEhGAuqWac3uxqB7rtE9HSFvB9Dt0TE0ZtEcw4wF3LlTwNv0zk5gA71VbLU9AzGWKDAjj1dSlt2arnrQuOWYurcHWG+s4hNHZ3LzzRpdwGiltLzc8bvIOwP0cvx7jhaprTxTA751hdgAfqnBcSbh6g9YXAaAeQXmaUzOOEwYHxBggP65i6M5EMTWLdIM5s1vQxx2zI6A3AP9M94DkEfNDqyjYQOWya0KHeBZN2hICn6pAZARwJ4glICYuiXsmHOYNZ2do6uTYW1PTIEmGU8Kc4ijhYJZbcCSkpyGH9PaYfkYEEpUYkmbqvUrBUr+e44+zIFKi5YX1lmiAHPXpOqt9SfuU0pS47dGy4Zt59qqgVerFt0gMXNTjLBkzWmUJS8+T53WoOe3HNZr4nGp9h2MJHB2E0Aeed9OPj8y8PhMJjNLE7gGhPfp82oKAroO9nqH6fEO4+N+NobjKDAuopKLzcvpi9glwRK5JLYE9byHequHePIc9oCF6gROLD3jANb+/nPAFltxztffAQjfGaAXg1hqosjtG5QmsdR1/tgdBgZXtcI/D+7HFz4Sh4EYjYt9yeHFpi3THRAABLB5YtHtLVOZPblg0qwJypEtNwF2XeifG6hbHjziOEEcOUIC7+WZsxfMzv56EwDFqYi08/WzcKlPAL21GF8ygAXkrUD3jK3PaLIuoayruwIQJf+nJilRZfHnEOzidyCnQV1Cf6be2bCvOQ2sVF/qnc3VlwKN1JhIPbschViiP1v6nALWlLRQlq2UqC97xzEdMzfxQu3MKd2Lf5clLf5xYgFqn7JHOMpTPpccKamTs4EKxoldEVzWEk8zugrO3X8c4M1hCzcda3hXCsNDDnANYE4JBDi3pglQHYEmOcfytM5dwnQ9bCfQu+0Ye3D3acWJi8MlmltIcc7zob5gzDl5Z4BeaTUbf28ZaDnqqEQTxW1HZVb75wH8ssqAbnTt08j5sUQnIEZnNen36KyzoJSsvbEvHtMTYuSoK/LoXiLB+4BqryEOGmLgjW+fU2+mR1L9AsAqnLPg8gYwzhrUKh4QrIlyeLPuBuhu2WcIOqBMUxpfHAj3LoYsOaAraSetgFai8dZSnLn3rjZhl/qZ0kZT4JdjQ3LPLpTUYjPuW07TjvsR1pnSvlqujaWF0kxpyPcgTQzF7BJAS7QlH6PVbzlYy65DmlmYk2M+somjMgGchhYL7ydeZLvMKXojMT6UOD4WGB8SxOjwqmPwEyOBJkDdErNCjsU0jsEZdwQrFcSw42DTAKzR8/YLtD7R+FLBrFvBkMGvqWhS7vTL/sRP/ASICN/zPd8zHzscDnjjjTfwyiuv4MGDB/jQhz6ET3/60yfXffKTn8QHP/hBXF1d4T3veQ++7/u+D1O4OmmV3MufoztyVEeN6sq1WdIGo++5Fz8bGR9gP7jQEkob4Og2qfcj5O04r8A4QgNmJ3QygBh4g7vbG6i9y1fltDp1q9E91+jfGqCejxCHCPD8xnUQnYEtSaO3zQ0ysqy9yYHBlTSgbgjdFwS6JxLdM0L/hPsgJgsyDiT9nkPgCuEjrIfPyP9L/yQZy7jwb/h71Gi9lt8+bmeNxO3H9cR9L2loqb6E95gqV9N6U9ek+h9LSavM3cOlC4YWSV2fWrzG7d6R3qwu0MLfJww+AZymCOs54hI6n8dyM+ey9JFbbPjue6vK0P/V/5Niscb0tGaQbNoqgXEncHyJMDwGDl8BHF8GDq9Y3P4lg+dfZbB/zWL/HsLhFYH9y4T9K4TDywLHx4TxmnB8JLF/dYPxlWvYqy1HjAoB3d9jArFKgFd0X7hALtb0/uAP/gD/+l//a3zd133dyfHv/d7vxS//8i/jF37hF/D48WN85CMfwbd+67fid37ndwAAWmt88IMfxGuvvYbf/d3fxac+9Sl8+MMfRtd1+LEf+7F1nbAGIFmfHFLXxWVyGmB8LiVrqJSzSws0Z2zVNU2AFByktVMQg4A8aGyeEbsvjDSvvAAGP92x4YsPNSaPGuTiZpKzwKKJfXnm8EReBLF1jHYcutfMgmgK5MMmGQNx1OieG/YV3LIFqdxz/7sbB3gDa3o0ObCbTo1WkpvZlUkkmTk8J7mJ8LTBNPitnYRrmlGLxH2pLcpS7EUKgHJAmGIzwu+p/pc0qZzkxklOc80tWhsWnKupyztQnX4sh/E2s/WHLklE7CAu5RJi0IPdtptzWJoNuyjRZCFuR4jDcGoIZqP9LhH43wUGI+S1LiNYe9R2XjTr3kLvFid489IIaMKoOIO72RDEcUn6LEdiW4EeLmh8B7nfQt4egHFctL0okkurdtdi7blGLgK958+f4x//43+Mf/Nv/g3+2T/7Z/PxJ0+e4Od+7ufwsY99DN/0Td8EAPj5n/95fM3XfA1+7/d+D+9///vxa7/2a/iTP/kT/MZv/AZeffVVfP3Xfz1+9Ed/FN///d+PH/qhH0Lf9+0diWlEfywu48/nBpIvF9eRo0jvifrgak/3AE76FjqMeqfPaXI+PLxKkwcFuRfobgTGKwL1TFmaDtBWMAViPMCZOeoKwNoZhFiCQ3vuvKOlPSHm1SN582etlzxe/qUdJ4j9CNVJdBuC7pgOATDvJYqJ6VUataNkOCrEbCyToFLvHIWlhb4+bzRdb057aakzV35tvS3lS+9qibbNgVCKvmxdPKSOt4yh+F5q5UoLzxbaNdXvFZKyyC4asaTEa3jedWC3gbnqoa+W9GCmY5cFeTDoaBmzZAxICNe24W0RD6xCzDk2YXhvcKY/nY2A3I/objp0zwT0jqCvLKAsjLAgaQFhIb9iAgmDw1tbyGdySUR9IHZ1soDoneX2VkF23qRc83aGlwTwnRi3xI8sBXh3CEJ90ZVvvPEGPvjBD+Kbv/mbT45/4hOfwDiOJ8e/+qu/Gu9973vx8Y9/HADw8Y9/HF/7tV+LV199dS7zgQ98AE+fPsUf//EfJ9s7Ho94+vTpyb9Z1qzG1672/DUlOsf/vSsVkxJH882bttbCjhNwPMLu2URYPtlj8/kjNm9p7P7cYPcZi+4m2LObAHnkcETy6GL5CYLZSowPOhxf3mB8vMX4rh2ml3YwVxteWV5tYB7soN91Bf3yNcyDHezV1sX77EBdt/j2uL0FujlAPh+g9gbdnkOOqT2/8+TMnucYgN5QRptTajN6nlUfp1gLSklukr7kfQjry03qqTZKVGZOi2p5p9aUjyf++F/LwjFXb4tG+P9v712DLTuuu/Bfd++9zzl3RjNjvWakICkOSSwUxwY7RB4gxb+wysK4AgF9SLlcwRVcUDFyysEhlRgCtkOBUqGKFA8nXwD7E6gSKg6U40AUOXZIkB9RLCLZoNjGiZSgkYytedy595y9d/f6f1jd+/Tp2927z7l3Rh7NWVUz95y9+7X77O7fWqvXIweUh/l9UurTElXmEVHKXSGaAcO92/7xgXUGF4oByhznNTm/eYr9Wxrs31xhcVJhfkpifkpicUqhO1GjP87qTqpYShzUozZcoZhOQDccg77xBPSNx6FvPA46NmNf38oF5iWIeY/J1zvMvm443dhCQO4LDiy/rwAB3Hnr13H3bS/g+OldmJmBS3tkGneW7x5eAEqA6orHI+TqswIroBUCnrAB7t2/geTy2Q5zqLe2pPfwww/jd3/3d/HZz372wL1z586haRqcOnVq5frp06dx7ty5oYwPeO6+uxejhx56CB/4wAfyA0ttLiF3V6KmibWRUwnlzlkKKBuY1nJrZA1GhrxzC842LoSAlBLTrwJq0aCfKZhGDCHFZM9GJNWehugNqJHojlXQE7b6JMmhjYQmqLlEpTiyisugYBTr+9VCQ84rVBeFTRhJq5EZtIHQBnLRobpUgaSA7CU7qNfOetRKmy1LeaTNylyvcHneRlaUUig29+FmmJKQcnXCa6H0P0ZHVcYvWwLiMdV8SnWZG08KTHJSnCs7JnXmxp5btymKSXi5fgqoVMvgH1PkEsv6YxgCTzjjFYBzYjY19LEJ+uM1uuMS7Q0SeuIFmjBg45JesRvBQkPsM2iCFEtyNhML7UyhT87Q79Tod9jqsr5Yo7qoIObdUuKy0l61X6O+KK1lpkALA32c96ev7h7DpXqCvcsTqMsSasFDlla96TKi857gnjViOOOMbYJQZI6GWKB+6iJn5ONUorTe7+jTWqD37LPP4t3vfjceeeQRTKfTjTtdl9773vfiPe95z/D94sWLuOOOO/gll3WaW06puty9sYXmfy5VjWxAo5Eb/ACwwOD/MgAfANlr1EQQullmSjBsViw1uyJACuhKot+RaI/JpXN6JTiSSyOgp3JIKgtgMEsG+CyhPzGF7GoGUGNVpS4RJPFntV9xJAfUMJWC7AzqywbNhQ5qd8Gqza5bRodwqs1EzMINJnT19xpTxeVU4GHZMXXjupSrP9b2mAoz9n2d8YfAlmMoSxmIEARzKtV11lqMwRmrH2MCgnKl72AYSD482zuwtocYq8ZKRk6KYfAiGzxeTziXXj/DkDNPas6jJ1ug3hUwlYSsK15XwrWnQNOGAe9YDT2V0FMxBIkmNYOa1xyTs7PGaL1BdVljckFCtWycQlLajOw1ds/X2CUBtS/QXBRD+LMhq0NHqPadMZteWmw6S2wHYhE/u+i5nQM8l07NGt4IQxCHWHNrgd7jjz+OF154Aa973euGa1pr/OZv/ib+zb/5N/hv/+2/oW1bnD9/fkXae/7553HmzBkAwJkzZ/CZz3xmpV1n3enKhDSZTDCZTNIDC1/aHHcbo5JFlQPS3Oc1+okujqBfIrZ85FtsAUmGz9uk1qjbnkMIGQCVdWK3hir6WANTSxvJxQaCloAmDl3WzyQEWavPfaDeN0MSWFMJdMcrBlSDQWpT8x5yv4LcswY2hoC2h5rzeN2hu1podqCddxAuJJLLkOysN4eMAliJPVkMfv5GmmJu/Dl19w8DWpvUTUmLOck012dM+vPXQepvSf+bMHYl7aTGcxgKmdWcytV/L3IAvCb5YLccynjwidWxszZFNwLdjkB3DDYHpV0b1n2ALbUFc73O2tqBQ12xpacU7EPbWx9aBc61OZOo9hTqixLqsgU+ItQXW8wsYyx7CdkJqLmEngKyxRCwXmrYGJ9Ac9llgyDIhUF1uYe6sG9dLDxDNXeWN1hpLyW5Qf3pzYGrI4SwoRfVco9IRHopobVA741vfCOefPLJlWs/+IM/iLvvvhs//uM/jjvuuAN1XePRRx/FAw88AAB4+umn8cwzz+Ds2bMAgLNnz+Kf/tN/ihdeeAG33norAOCRRx7BiRMncM8992z2FLHNbox7Tkl2pZQCv1x7I/1EuUL/mRwgSCxVnb5PjOYoKlQp+OcEbhGRtc4ShiOxq87Fx2Q1Z3cM6I+x4cvkawJ6V9roLgCsE6pu+J8gdnBvLkmoaYVaCcjznIZIGMPOtJ2GuswOPUJriEXPEuGi5bM865y+EpLImwue2jWkvdxvkFILloBWCTCWSmW5PlPSZ0l/4fOV/g37S40pN3clczqmWs2toXXXZw7sYv0dkcYmzJBelFbIS4w6nG25v2qZRNZMCMYGnFaSoy2ZCjZXpcCKGlHweRrVCi50mLEJafup1dwYgdpGXmkqicoCn+gM6ostZFtBaAXVSlT7HNKQGeDlP7UgNJc0mgsdZMuBLERvmLGdLzgepwtlmHv2xL3BRWow8FGek3udrjtCa4HeDTfcgFe/+tUr144dO4abbrppuP6Od7wD73nPe3DjjTfixIkT+OEf/mGcPXsWb3jDGwAAb3rTm3DPPffgB37gB/AzP/MzOHfuHH7yJ38SDz74YF6ay1GoLhmTynxKcb8pTjjWXmqDLR5+5tzA31hcm5ZLdFIfDfExCeh7Nnd2h71KgioFMWmGJLGkbNxODeip5SIbjqCip3worafsZ2eeB5pdDPE6TYXBHUFPAN1UaHYNn/k1NbDogPkCYtHaiO1yaQFqDNB2oNbl9DKrYcfC+T4shVx9TMVd0l9J+aOUHsP2UlQqGabqjakWS1WWubJuXDFJb6y+I1u/2Io3NcbUWI6AxoJPrADgylo2NkYlG6wJa10pej464LNwPl8zDYEqgtF8tucMRzgOr1gGpleSk0s3FaiSgASokmw5bQT6htcyVYCp1ACu1V5vIzL11uBModqvOA2YYE0QBFDt8XoXhiBaw/E+O83W2Mawn6/viiQkIM2qBadPwbkdfxRLda9wDvtWNdrUuKqGLGP0sz/7s5BS4oEHHsBiscD999+Pn/u5nxvuK6Xw0Y9+FO985ztx9uxZHDt2DG9/+9vxUz/1U4frOFzIsYUdU2OUcnvrqEtLwBZ+kfh5QKTggfZdRHanKhRNzdyV5INsUVUsEdozM2Fz6xmb2NXUwOJGA32DZlteLQADmB1Cf5yT005eZL1+d4wgtVu4QNcLNJcAQQLV8Qpy0UC2HdBy1HW07erLaY1xfO7PxfOMbXqHirWZU6mFZXLqwVi9kMJ3o1SKSrVdApolKsPY2GLjSzF2sTI5aTTVbwnIFKyntdXc6/YzVjdBKTBOqjhT7Rsb9q9nDUlzqYKesPqyhQAgbOxajpgCA3YLCs38hRgAb5AWbaYVgBnc7jhsjE3AVBLdrEazqzD5uoTat2mN2h7VpQWc83pTScCQtb5e9ellcJW8FxlasQx1acKEskMIg9eH8+YBnqgsRCnFbUoJqiuYenPoOjTofeITn1j5Pp1O8cEPfhAf/OAHk3XuuusufOxjHzts1/lFH94vWYQxQEtxw6ny4fU1zixCg5Ys+HmZs50hkzCGXRqcasDQYOG5TEdks5s3NsSQYNWJ3OlRNRrtXg20EqIxoEqgu5lgJorPD2qCnLNjquwEpDWiMkosU5AMWRpoOKtbcUJ1L/whdPKJybOTkGFiYiBXqqIrkfLGJIyQxt7DHJVKnev0vY66Lze/qT6zzR0yoPhqY+lxrVu3gFKAl1zPw3GFZH86l07IcAAK0XH822q3QjOR6CcK1RyAAIRmS0k15xi6wnA90fUra8olezaNgqkEO5Vb60/y3At0AwAuy4oEUGPydUBp4hi8VmIbdiHns2sIZCVKaiqYxsYD7TSk/QtDHBN0vuCcexqD9flKCELPMtOFX0NTQwwuCvyXlAQqBTNt0E833z+u7dibZLxfw1IK3GJSXliuRJorOWtYU9KLUZGZftA2EfHLbsxg1Yle8Oeu4nid/XAHJAVUB9QXJDozQTsxwERDTDWcD45oCPqU06MAuhLQxwTUZckZFlzf0roxaKduNUMINeLBRU2TYxabo+cguTkJKaa+dtdL2y0tmzvLirWZkwjH1KUx6TQluaaeIbwf1o9Jbn69MaYvVi/yOwkXZ1KaqNbj0LTJetzgiIKrrTqox8qsdOMM04SwfquSgabrIfd71LsKzVSA1Gqqn2pOqPbNECR+BUTcPyWgJ3I4t++nfB4PwRKicGE6J/bYowKEkZBdxdlWtMu0wu5IQ7tOzSgEqFbQOzXMRFkjuQrVnvXd1YTqvOC62ti17qs9PbADlmHRnIN+vfQ/pEkFUgqoJPS0Qt9sELbS0rUNeo5iG1vqDCF8mUvVIKkNJMbZHsH5QVbaCwHcswQjrdnHBViqPgGg7SB356guN+hnkoNQaz4vqHcFZC/QzwT6UwSx08PMK4i5BNUEMesh7VmCVIS66bGY1dDdBLjIXCJbiBk2m3ZgFkh1B6ItJM7xNtroUlJGiqFJXYu168rG3rOxc68UYJX0576XSnR+Xzl1Zo5Kz/FidXLvegbwRFWx2qrr4TbFI5X8RsYQpYJyY3kxRzU2bljOGM0xjV0HoSTkXEEuKlT7FbR1AAfE0jXApvsRC70MEO989GwWBFMz0LU3CLQnOMelnrAhDEt37Fyu5qytEcb6/7UVhwu0Lkl2oMNfX8rrdyr0x9RgdOP8AWXHTLjqNUTfg+y7zElwg/ldATxnfTphKXJWw9QKppHQEwnTsP/gpnRtg164oa2jlllH7x8DytS1XDuF5KtFkgsn7MtJXTasEDcgBomP2g5if4Fqt0U9kwAUOiM4ULRmwxSSgHixgtlVqDrOxq4nBLNooCcGEAQtAH2phpxLqD2Bao8wuWjQXOwg9lvOs2doaVyDAOxiESpWpqzcanNlYwwllFTZEBhCiWZZIQ4cMSknJ4WtqeY70F+qD/96KKnFxhnWiamDU2dzpRJsOJ+psQS/j++GE1pALpte4zzPf1afUozLhjQ2puLM6cDSwpEscPVsCS33e1R7FUzDbag5F6v2CWpOkAsb3ajXXhsEseghFxqyrYAda72tgPaEYUa24yMKEPgsnwBhgH7G54XCVBAGqIkgnfrVtk2V4qgxsxp6p4KZyMEyVE94nLJnaVToCSadhmqtqlRI+NLeoNJ0Ngg25qg5NoPZqaGPsVO9qTnIBVuaC5j5N5AhyzcEhQvuMDp+IN5WCviOkNbidL1zApJYJlp0juxKA/MF1IuXMQGg9huoGxT0RKI9DggjoBZ8sN3vCM50Lr1D832WHuVCcJgiDVS7nLevudCzD96i5QDSbvH56syCoNDrcvYHymfmf6XsOhJejkrAIVcvBay5sYxJjjmNROx7TBWae54cc5caV2YtkiEIaIS/fAgUxcxQqbS5BvAdRuosAjxjgzuTteAUhpnUSkHuLVDVLuYmj1V21h9uX1vLSS+1kLFHHFpDznsI3VhAY8tN2QtoBZiZgZkB6rKEbMXgYA7is/5uR0J2CrJj1wCpzfI3kpIlsAmnJDK1sOeG7Ebhgl2QFJCdRDWr2bJ7vkhN0qpKczaBPjnF4hUN2hMSvQVSU8POAw1uHZvQyxP0xoBo7EVPgeaYCtUvn1tQmXsllmBJ8tr0w5ZBCFDbDbEuq7aDujRFtTeDmSjUlyr0OxyWrDsuAOIoEKYmjptpgU92gGrZKdU5r09e7NF8bR/ywmXQ/hzo2kHKO+B/l9iEY9ErjiRbujd3yUwMJRL7wQbHpbgS1WMM+HL95c7OSseeA9uxdTEGnCXakBHKqQIP/S6kjjwKxnQYSvrtkWENjR+dBYAzBqP5AgKAAjDRhi0ylY221GnOsnB5n92AjGEtC8AO6T3nx6zmGtXCWnHWAAmB7gagawCb/pzbs6AniPNxAsTA13OfFcAJa4kzJuhZjf4YB6vgMfM/UhacbBumZstP2DiafPYvhmDVTsoT1kiFZhP0J2doT9Wc2++43YsqZ3TDDDpVW9BbpZx0V8Ld5RZqDuj8+7ENItfG0FR8sy8y8HDjCKM9OBAaIrhoiEWLan8BmjSoZg30rEZ3oobqFEQv0XY2PRAY7OpdzpOnOk5OW+1xWLHqxT2IS3ug+YIBz57p0SDtpf0Px1IFHdWZTjL+4abnX6mNv7ROyfWxca4zxvB7uBbGtCGhJBd75rHnK5DCw0gmK3VoVf1ZRIm1fljJLVc3dT+7fhN+t9aWjMMMavaDpYa3bLHfsgP4omUXIJePlMyK1XZ1qUY1VdaXD6j2OBGs7CpQxWd7sgXUglWSLloTCQxWn3z0MUEtBESnYRqF9lQDPZWALUfSAZ41lHGvrMBgXHeA3Hw4kG9qzioxq9jatGJ1ZncMnOrIZnInAaDefG94eYLeUUh3fr3Uws2cIY32mQHfkojto0Gq3VcnbVkDl+GsTWt2ZF+0EIsG4nIFtTdBdXmCaq9Gc1mhPcZ9VAvOtq4WnCWhutyzM/qFPYhLl0Fty2rUGOBlVJklFm9HTjFJJ6VudPfG6o21UUpjqssxKjmTy73Pfju5tRADy1yd4uHnGbtisEqtLXs9m+OuYIzr3I85rCfDkgGDmhPGsPuCrScqBSw4jyaIgK63bgB6CXjG5bgkoAUgJeQFhXrK27zs5RBNSWpWR3LgamZsjWIfckgMRi48JrYCVQsFSQQzY82Q8//TDefx1NbvD8Rtip7Vqs4pfeVsfwA8u3/adEr98QZ6JjnajPdz6AlBHzOATXNkqkSUlwK69kGv5GUdU0mmyuU2hxj3nTtHDKlk8xm6inOOy6Yi1p2+2sRddupOrUFa2px2NvizUpC7e6j3dlDtTtHs1JjZhJUAOGllqyH2O8i9OXOYi4WNrEJDhJVYSLHcc2XPago3omIaA64UaJVIMbk21lVhlgJpCtxK1J6587YxMByTbGPS45qgsuLPNl5ptc+StR4b8xWmooAThjwxyUpsZEC9ZMe8VlnLaL3iCztIUlIOVtvUdRB7c9QvcgQTQRWrBqXkMGJWFelUhbLDIE3VCz47HMZOgGkkqKqhJwr9lA1XTA3oiVWd2vZUCwZdAWthajjeLoAVZ3RDQO2MWNjPz1Qc+aWfSg67VnE7LKYSqmMd6qZHVyXOBwvo2gY9Mpxlwf8eW2glYLTm2cPaC3rDTaCEspnXQ8nP7Qe+G4F1aIeSQMegJpuarbRclHS7wMT+gs8H/YDRjjMN/e7WkFKiXHyJJJKiWJncb58CjHVUeGPlUn36412nbgpgw3Gk5iKnwYj1mesjrH/IdztkiLJSXinIlYxpzbGXSJ9F5/HebzHklfMCy0NKoAeffRn/vNwAkc8kBEQvQEJC7O6hEgLCTAGqAAJUK9BPBLQRoH6ZJX1wXu/ZWE12rN0BwH54UqA7JrF4hcTiFUB7gg1nqj22BNXTpTU42wHQkMXBhUkcPku2N/CDTbtUaMPcGR6LWgC6E1CVxvHZAvPuevfTcxSCW+7cYh1pbOzco2RzjY1tnbGM0AHVSWwOfBNwX+0ZSn9tN5gQQ3DcO9IGMAxy/Nmsgh1QJN35NObnVPjg+fmOd5wGl9J7YwDl/96xdy8GWDHwHOsnBOsYkKXez9R7HXs3U1JorE74rqfUoAmKvRfD+7HOWomNfWzdrbkOSwEvppnJMquABSI7xzZND/kgN5Q3q59trFvSBgItaC44W7gEgCmEVtBTxQHopYCxZ3DCvpayB/vnuZyZ4O9USRu0ns/ZFjca4JYF9G4N2SmoPYH6kkBzkV0qqrn11evNMlO793yrE0nLgNXWAlV2AkoSSLF9gegk+q5CryUqVc5Qh3Rtg946Elnq5Y4tyJiqJwV4637fRJ1UQFnViaPYyybFAQAczuRkB+frx+0RhlxYFuwO5MCKbNBjBitrU0pKiakUc5tdTE2X29xzAJVTIYbtjklqqX5KwKREjRnOT26MYd+xeYu1HT5LikGMUJYRWmetxNZwjom9AjRm9BKpsPxsJAieUZqfWSWSbTw8v1/Ju2kI0jAAyXYCvagg+2pIMcZniSzlqZYANrIcUhNJTSAy9r7A9OsCppGY1411a2KLzPoyG73Ve5xDs7rcc7JarZcq2ZXnDewXNEHNDeRU8hmjJ9CpuUB/qcZFMUOVytxQQNc26KUo9zKHL3vscynA+Ys8BZahxBW7t8lzDEVWueLoQoq148aivTIOAJ3qwVtkQLCwnMWZNgfbzo0lGPOhKAV+uc0udr9EpZkqG6vjvseAKdV+6rs//hiI5sYUjjvGdIXvckp6i40nNu7UXF9pWrefqzWulS43NLN3a8WtN59RtXQw2pGV+BzwoQWR4bRfbQexMwGI1Z2mlvDDOYpB0hRsuDbXkAsGLKo45+YOASAF0bP/bn2ZVZlqzvn1qsvsRyjn3dJ/1zfqIcKQF0IpToXkorLAuT3Y23OgloDQAqaW0LJG126exPzlBXqbqDBLKAVgmyy0lISyZluhRWdoJZaM3pJSl5Fh4xc3Fteu5zY8cJ3GAz5//F672bBLmwBeCbOSqhNjUnxKSTdh+zlwSfUfq5fqJ0ZjDFxpmynwy83L2LhS/ae0KLHxHAXF1k9Koivp9xDrMd/swXPK1P1hHMsODq47n8K2fD9dgIHPdPxXcxSXygCyb6CtdSesZDc4s2vOgC7nPdDbsSgbvUkAk4lkZRCxdMgZ023S6NZwRJi9lo9MjF4dm3sOm4yBDVlYfapnEu1xVmnK1oLpgn311FwAQoF6hU3p2ge93AuaUyXm6sUWa0m9dTjvsWuxsaxcXj0jCKW90fREqb5yZfyFl5GAUi4X8SbXyJHm+oltYjFVX+pZ1gGHsTnZhNYBvNjzxpi7lCrSLxOqF2NaiLFxx+ZnTKUaG3fuHcv1nVJjxlSrsfZyADk2hgNDTQeSyF0/4I/o3U/SYZgGZxAjBESLwa9PagMxbyCnE0Atnci5MAEGkHsLTvzsDGQqBaVt1BcA1VRxYOl9zeeAbnjaQF1eLA3frFFdLP4u9T0DIyYcuLpiJ3ZnxGqapXWo0IDsBEx/PTunj72sucVVwg2mONiSMaU24tj3Q1DKN2it2H+Ic6KjJuSRa2G9HKit7aoQkzD8v2FZf4zhbxl+Tkl7sTpHRTnp0gfcEGBy4J+j1NzmmLqcSrdk/LHfKLcWct9zUnkKWMc0Mxuuw5gPXvg5LJ9NOXRYcscSYb8utqmUS8d3kqD5HKLvIRYdh/9SFvRs4GvnDzhYXAoB0SuIXrO0uNCgCTu+y1azP54E5/nrNOTlfdDl/aWlt29lCrDPBBkOndZy9nWhCcKwIQwpDGHNAAY8MwH0zACXN5+max/0fIqps3IvdGqDS4FnuPDGuNUcIKc27w25zhyFADSmYol99sunfARjILuRKrNE8o0Bgl8/Jt3H+sn9joehFMD690vqpu7lmLGUpJXrNyYJhnX9uUoxdrF3OiyX67/ktxhbV6nPR8hobkKxpLIbA15qnoHVrCt+0Hdn4OJSj5mOr/c90CoIKZdZzF2SZ2u1PUiAvYboJUSvoebtEHzadsL/euva1HWrgOfOGd0juHM9o4GWA9araQXZVagWNKRBQsduEKbijBA0MTDt5mv22gY9MgC8RViyyFLqkdjnsEwM+MbKjfW/jvSw5qINASp1/ueXD6+lyoWLtRTcNgojlQKwlISW4vbDOmE7fvnwc6xs+iEPthMDwVJppRS019nQS5irXLtj735uU47Vi/VXwrT644lJqCVzcpXAMBZjdoxpDOsn7/vnfTkQdcBjDKf3aTubqdwGlFYSKxaVoZWkkCAjbFALC5Qu0SswpDeitl2qLl2/7q+fcV0b9j3vNUTbsYTYEWQPtMdt8OqerFWnQH1RoO8qiPn16qeX40xLzwzGgDG28ca44jGQHVPZ5NrIjTVDpWdrsTBnY21uaqiyluQ3BmRhuVT5WB2/XkriDseQaiek1PjC+zGAyElrJW2XUqkmZKxOqfQXk0x9Sq2vTQApXJ8pKTYn3RbQJlbIIfD5bY1JfFnL7NDAzH23dVZcizzLTrhM5n1vM7N475L/bEP7Guy9LgFtlolt/bNAwPr9mlXpzrk8eeNwuT+dtCd3F6ibCqQa9FObR5AwhEubfE2gmgLtgezh5XRtg56jlPRWsthi7eRUPP7nlLSQWqwxaSI25lwb/jjXpKP0l7sicTJzUngpExO255cb24xj9daVomJSTg6ocmAe3o/VTY2t5B0pWQ8l7ackzpSEG7adkmzHwDzFOIaUk5QPQUe5Bori6ubIc2tY+ZwsbwYndveZVaAH3ZPYd9csgdOBH8AA6EuXTpvkh0bzk0n7yWNdnj4DQLBUKNoO6tICjRSAAWd/mQh0MwFFLAFKzbE+N6WXB+iVvNSpMilJzL+X6zdVJtwQcn2XjvmQnOk6dEUyV493Gr+eUlfGNtlYm/6GfBRAGI51DNRS92JjHwOJ2EYf1o89W/gsubkO247Vi40hBVyxMmPAlvrNfUqtO/93TLVbwlweIZVGH4oZuoySP/6cMUssB10IRF75bJeDRBf89dtdFo734znYC63ZaqWzwLcrUEsBiGp4NqNsgOyOoA/xc708QG+MxhZ4qkxsU8xdD9tOcf3h9dKNsYTG+i1upmxhXlEK5yjFSOQ2uBiAhO2HZXLgGdZNSfMxim22Y99zf8O2Y/2tSympcqy9dYAtBWapvkqeIwWuqXGu2/4hacy1IWbkUtjw6vecWhNYBR4h+DwOWDnrg5QHIr6En1fIr7ssfLDfGPC5a5LPCQlgxaUQUFYSFbpCLWz6IilgGgEhMutshK5t0CMDto/dYFEAZfVy6qoY11jaXsiRpkBw3fZT4xyjMVD07l9VCbCUW/ev+3VLnt1vN+w3JfnFxpLb5HNSZKxObPybaiRKAGFM+ittPyybYjpia8Avl3rGHPD7z3cVpLfDUMzI7EjcFoADMXaHczOAgc4HKA/gAHAGBi/UmbsXgqe7T0QHgawkNqhfzhiQd77ngE91PeSigbpcc1Z5JUC1AikJWXVrTYlP1zboAfENp3RTKFlcsTp+v/441rnvX4t9zwFqThooUTHF6LD3CzajUQkx1UYOjGKfx54hBUr+/XA+15HqY4xN7FnGxpeSZkulmtL7pWVKKPdO5+Yodc1dD3+HlLSY6suVXeM5j0KjURKYwTckC605s4Emlg1FVZoDULm/PsD5QOhJhwJspHKgNTIrhi1RVakDs1hQ7Bh5wAvA8yE0IKOBrmNV5zB+lkw5+8v1Gnszp6oIuUdH/kKIAV5pnzlVTIw7H5PeYgs+tvm7ccfqx8YZtj1GKUZgrJ3UOFaaKXRET21wsbq5dvwyuc3R/Q1//zHVXvhbpfr0y8b6WQfMrrb0skl/Y8xL+B6XMK6p67lyJWWzj5HfuEtDj41dHwtJNgp4jhxweSDn/nL6HnGwjrsWGoaIoGz4zmre04RQccfzYUyRPTVWxvff08Ymn9VLlwfJcTmFlPbf9eqykKNwAaUWQky1kpMiUkCUohzYxTbHWNnYM5X07ZdNAX/YTukGdFSU2hxzv1MKTDbpO9aefy32O+TaS0mR4W89Jp0WSM7fsJRiNnPvtP89ZBLd9dT6yfV7BekopMBYe0VqzvAZfcBzAGI/C3d2p+TBeXPtBGeBXN0783MgaKUxEt5vJAUbogAgB2guG0uKfKlw5Tn4u0t0C9N7lqEACZZECdezejPFwfv3StuJAYN/L7ep+WNIqVZyaqvYphiOZUwKiJWPAXmsrXU3pZz0NSaZlUh2MdVXilKbYUyaiP0usXZi38eul7aVA9KS592UrgSAxt5TIC9lxd6h1JpJvX+pcuu8N+vMx0jZTeJwcrMHfWn9v1mKABVXXgKfUBJQCqKul8BlM0kPLgl+JBZgaVgiJaiugEotQUxzFnTRdksAdJlWbMSXMJN71NDF/xzz47PO80J4/oFC2pyABtDXs6SX4pZzG6krn3qJY22WAGhKqkqB4diGuo6049+PccthfzGQG2MgSjjp0ntj0lxqbGOUmqNNQKqk3RiVMDGxeYr1eQQ0bLxXUgJKMU2xMjyog9JsijEJ24+9745y72iK8SsBv5H76wBerMzaYcnIcFYU144v4UkJUVWAkgx2VcWxNSuW2kTXc6gwYxhk1LIeKWnjcCqOq1mrpQM6AbLTEG0P0dXsXmABkHqs+u85kEupPaOT4T13YFBzlHTtg15OMkktjtgCym1CKSnCl6ByUkwKoGL9rSvVOQrVZjnOdwyIY1JvboPYRIJIzXcOeGObXuq5xij2zOF9fyPO1Ql/09Q7kQO+sfkI211jvq+otW3ufR4bZwrExoAtRmN95cD4CjEDsZi3KV+9WIgy/3qS/DM8IdgCUgqgqSHqGjRtgEkDM61gpjVgCGqvHRK7CkMMakIAdQUzq2GaClTJIc+eC/gse4LRCrKrOFzYogfmHYRSDHx2SILMap7NwFo057O3IqECfGboPeuB8hvQtQ96Jdx3CuBiG3lqAaY2xRD4cpJcTpKJ9ZW6nwK02NjCeUrNR2r+UmDq05gEV3IvdT8mEaTGGgOaEukj1o9/L9b+2LhjYJXqI8VcxX5r/9mOkjZhXHyKvV9+uynATq3fGNORmo8ck1EyZ4d4diEFWx1m6sfSfh1oIxGR5QDgCRlXazoVpRRA3UA0DWg2gTkxQ3diAj2T0I3k3HTHKtQXWwYtmyePKgm900DPKugpgx1JAVNxoGcAUJ3Nl6cVZKuglISUEnLO6WAF2eybfQ/OSyRXpb1QxTk8k6eOdQY3/lni2J6zJl3boEcGHK10jc10TDIL6/l/XfnY9RzA+XVT9/y2U4CVW9i5/lNSZaxcasx+O2Pce+m93G+RkprGQCpkPnJAGWs3xSiEzE3YRo7ZSc3BOpLfFZJG/PaLTPRT726OQShlylLzGGMuY+/22NynKDG3xdaZQf1c5hK/3Y2irwAHXQekBIRYqjQnDej4DOb4FIubplicVNATAV2D0/bMJUwj0VzgAM8AQLVCe7JGd1yhnwhAgC0oB29xcNYDAPUeWeDg/4UxnB6o6yEUB6T2w5itUOgcb6+tgJ2z1BRWAq0sTLksDq7PDenaBj1HJRtzDKByEl/Y/iaLKyZNlXCqYfkSUPefIzb+2POtKz2Em0+szVjbufb8dmNjygG2K+/Xy0lipVKW+54C4xwzEY4z1Xf4u6YYgkPQmI8Yd7Om6jO1rlJSXOn9secN5zxWfoxxuEoUqifDe7Fy/r1RAPSjrTjAs9aZoq5Bswn0yRnakw3aGyTaGwSD3gQQGqAKfGanBKo9Bj09kWhPKLTHuCwEhqzoJLge7F92kZMQQznN4FkplvJC1aN/3njgWeSgykRVsdFNVXEdd94o5XCuKNz89dernx6Qloj8eymgiXGVMbVKimNdZ3y5zXgMEH0a43BzwOnfd/2G13LA6Jfxx54ql2ojNgf+55xUltpIYyCVmvPUGMbeCfc59x6kxpbboHPge4VoLH1UMY1J6v71lJQWY6bC9mNr+wgYgxyVzEeKsSidyxTQDYAYA3sjPZ87CygOLKZOpdmgPVlhcVKg2xEwDaCnAAigSkA3AiQEaiu9dTsSeoqhrOyXZ3mQFuw6glBAJ1jlKTvB19saojds2CLtnqAsAMZUmqGEZ6U70dRAXS2tRm1SW1KCM8gpa9iiCdRdr5JeuOmuo2LJSW+p67ENeUwaCe+lQCm28ceAI7Up+3/HOOCx5/TrjEmPsWdZh2KSTm7OY59zzxGCWdhvDmBz442NJ/YcfnslUnUOANakWA7EK0Kx9zTHtMUA0b9X8m7FGIarINmlclSm7rtrftmYFDgakiz2bP45WFUBdQ1zwwz6eMPZCWqwhDcFTAOQYqmtB38GCZCSIMGA2M/EkK2cXHeC6wCc3UAuACVZ/CMByF7ANBJUyaU60lUVYoiyciD1kAO8ugKqCqKqQLMJ0NSgWsFM2JiGpADU6lyIzkC3m7/L1zbokcGBeDm5DSbGfYfcfIm0EI4hdz23gcbG69dNSWlh3VjfsXs5Ljs29jGpo1QqGQPh0vHHfrdUfyVA6EvZqTZCEAqfeYxZiT1vyQa+JsWMIWLnRlcsJRR3sHot9k77c56au/C3SbVzhSW9TSk29z6FoBaWXwG8cG7DQNJOylMSNG2gb5iiO1ahm3FKHm3BjoGMAAloBY5jKb2/lS2jwNKgBCAAU/NnMyGIDqiUACAgDKcEIgnoRkLVCtJKZuzjJwbvhRV1px/LU7H/n6gqzr4+aWB2GuidCrqWMBMJUwlrXQrusweEJpjF9azeBPILoUQVlQMJ1+46NLbp5tovkST8TTi2YcYkm5JnHdtcUpt16nuKSkC0RFoL74fP4tcZA8wSZiclcaakVPe9BGT9dtZ830KpIGYafyRgVwLouWeIXUsxHGHbOYm/dKwjFGMQclFTYmdyubBjOZeFohibsTG7c6+qAk0a6IlCvyPRHRPQU8Hnd246awx2KVqyqwIJ2Hx2S4mOlP0uAT0h9McN6JiG2K1YsusAbQREbyU4AVAjQU0FUSmIjlWnkLYRp950Ep+zMnV+hDVLefpYg+5Eg/6Ygm4E+qkcQBfgccqOrUjN3uYJ9a590Fv35U6pnGLt5r7n2i4p65cf63tMOktJgmEbuQ0qBMWcVHPU3PUYgKSk89y8pJiOEqlunXcktxkfsTSXIt/Hy33PUegDFpMEo1LKGJDlfp8cIxqb7xgTU9JXbqwjlFJLpq7FmI1Y+TE1c/j7JSl0VfDjY9YVO5Pb8zoHWsSCGUgSyF7jcz2W+mQnBmMVwEp7FUEYAVMRg2ZDkBMN2q1gGoI21m+vA3TN0h9Jm4HdjSvmp+eHRXO/T1WBmhpmUqM/3qA/rtAeYym132HjG3L4RoCaA2qBtGFMAV37oJeSdFJcZW7TjLWdkzBS7YftxjblnCosfLbY86XGXSIlhQAXjqdEhXSUm3dqDkqAPSUJh5/D+ymJKwfsuWcu3XQPMWclPl65cyM/mn+sjdxZlXcj/2xjUrZf1v8bY7DGpHv/75WmnIReSCnmZJMILMvoK4JVmy5UGKzkNoybAY0UQGp5FkfKTq8CyFlk2p9cKwGjyKpFCaIVoK9NAAD9jCA7B6ruDNBJdlgCsZKDD+ABcupPFw+0UjDTCnom0U9spvRjfBapG4Ccx4Lk79W+gNw89ObLAPSAg5t4qP5Lqb78uql2SzfRcCy5ccYkq7DNcAy5PmP1cvfWkUhTwFey2awjJeUkhBjgh+NLjXtsHGF//vfwczj+VNtHtBGXRuXwVWMl0odPMaforKQY+z1i0rn/fbXD2CAOli1ltq6A5DzMmd+uFADUio/cgXFFL+fVmmsMatUZ3Rh7JibZiKVSABFEzw7kMGxhqSeAnhJMTTA1IMieixmwRaR7VM3XHGBSJaBBkEpALPjcr7+pA/US5rKAMMF75gxUwr9urLFHsoYvVElQw87zeiLQT63xTc2AZ2paqlwrAEJAtOupgX26tkGvBAxSElKqbgmIpTbJ3IKOjTm2caQk11Rbqect3Xhifccop6KKXQ/VUam2xsYalll3rmMbdK691Fz59cJnyz3zmptySkJz11JnP6kzplx7fjm/fFLNGXv2FINS8vvE7q8rwcWYsg3m/MCceiCz4midYiLMwfcylh6oRIUaGeDBa756T+tBnSi1gewJykpCpAimIVZbTgxIEkQrofYkpAZEx2pK0FIiBADZAqIXMJoBkRSgL1eAIEA4gGTnddkRfzaA70AefRQ/x59g61Hnh2cqwCgAktWs7GZB0BMa/ASpYTDE5nYs1zjoOUpthCmQi5WPAUsKANdRscW+l1Bs8cckn1gfKVBLjTNst0RySTESJZtN+Aw5wB9rY2xcpZJqyW+UY6hiZTPzMWZJGZPyYhvomATol4uZxcfALanmLJGWQ+BLrYMY4zPGgITXUuWOGPCGcyg/eLLnf8bZw8UBKTAVcSUEwOJYm2HosQP3AdEZBggwPpG0/yYG8liHetKjaytoNJAXpM1awEQVA59TdQoC0LqbwORFdm9QcwxO6qolyJ4gOgPZciBr0uagRGyNWIiIE9UCg/UmKQGjLAhKBj5BgFFWQp3Z37kygCKYhYJo19xPPbq2QY+sjD7GYcbqpSQN/1pMBefXDxd1rL2wn1Da8DfGlDSVk0pT4yildSWT3IYUK5vjwHNqMtdH7rlSzMVYOzmwDMcSY5bC77G6I/NT4t/lrh/oz46/RNqLtZWzShwD4gP3Y2tkjIlJaQxi6yGsU6r2LKAD8+XqSxvhBGB3AD8Bq6Eh8j/ZHHKCiD9LwRKfN+7YnOcMV9a13gQZkI1v6UJzqdZAdhKiF+xWoAjyWIdvuvU8bp7t4oW9G/DH8xth9iRED2BiAVIxmMEAwlipy/7cqgXEPgOoWgBqQfy3I8jWQO73wKK16YbsfmRoGXtTBO+xkFix5gQGYxphANGzX2BXEdAY1LMOk2mHE7M5XtzdweXrXr2ZktLctZJNPHUt9jlc5CUcvz+ulKRYAnj+vVTbMSCLlUkxCimQT40/RSWSQQokSqXSWD+pjTT1+4VjWnPzzD7HCPlGJFGH5FJQ8NrLXSsFxRhFr8cALlUmx9SN3RuT1nPMVYIOAJ4v3Q2hvRQ7TyvJEp3Wg2uwQA30PajruawxbB0ZAB83v+qLV+K/l5wbYDVTgdYQvWZ3BMMRS6TmrAhCL9uTgtCaCpOqh5ppmKpigbURrEIkQLZsJKJaBkKhOTrL4KDeMeDVlwnVPkHtG1R7HeS8hej6AYABrMbe9KVkxyCgHq4JJ1mC/8reqlgNIBoNqQxqxXM/m7TYm2wOXdc26JEBhIovuNgGF9Ydo5SUF2sntkmn+ompcVJA5vcTA6rYpliy6FPjyqnwYlJqqq1YG7ExxeYiHE/pb5hiesJrqe+HIKHUWlJSzvR9GJuTHHzyzdYjm+tGY88B72YNll3PMY8pQCthLjO/a1TlGAKeUgxiTQ0xsVFClIToNdD1vHFXFSAFaL7gmPedl9Q0+G1iZ6XhmJJjGyHSBkKxRCV6A6H5nI3P29jKUmgB+voEf3j5VqA2EJUB7VeQ4HMzELsviIWNt2mlPOnOzQwA67xe7TMYqQWh2jeoL3WQuy3EouN4mIYZA1ZxLgHQTi5AVlJ2UqAx9iwQFqwFtAVbEEA1QUmC0RK7exPs7k3sc2+d0w9uxinpLwUMMTVNbEGWbJSpjTsHZqEkldro15G0UmOK9R9y0zkJw2+vhMMek7TDtsYkVf9vyCyEv3uKCUj99ikQHmEkNgW8A22Gm13ufMfVj73jI2PMxXxcm3JaiVy5IwDsgUb6zklYQxl3fldXnJ5n0jDgTRvw2RMHVRZdvwz0XNcgK/0RAKH1gewCoXVt7oz1ANiFc2QZHnLqTCFYcurtP2MgDEHNCfUe0O0BJCRkD4heskHKlI1RSC798mQrIHuOpSmIH2Y43xMMQvXcgt2CoFqCmhtUlxaQl/eBrgf1ff43XQlHxmpZoQ2E1pCdgWpZehQG6HYAYYQ1puF50a0EOgnRSagXF9nfO0cvD9ALqXQxlagQ3ecxsIlJayk1aQoUw/tjm39K+klJbK5euJGHwJdqKwUOpQCYA9V1KKUKy81PTgoeky42GFvMMCQKdr7EF0anD40lhuuB0URMSkqoPq9YSLKxNRRj+nJSfowJjdEaqsxlt5HfwRCgXHoeBVEpdvieNqCmskGPK8i252OuuuJ0Olovc79pK9kUSCE5n8i1JG4iPmdcLCCkhKwUZFNBNRL1ZYHmvIRcCM8VwcbLtCHHaIdgJAbrSFOztOekPNFbVWPPZ3jVPhuuqLlBfamF2FvwO9r3PId9f1DK8xPIEi0d1fueg1Qveqh5D7XPvwVJCdUKqBaoLgl0soFp2DpULiTUXAC7m2tpXj6gF1MvpjbTnIqyVJVW2qYbS6x8TGqJLfQcuJX0FbueAo5c3ZSUlaIUmKdAcx2K/d5+e2OqsnX7LSzr1JzJjcu2I6yp9vK6wIEDf2AZVNjzdRqsBYFV8HNgOPK7xFwb1qYYyKa0Aeu06erlmKux8SQoC/TOWRqwfm8VB2NuKuhjDfRUQWhCtSsgh+giYMlwOgHajjd8Y9jIReKA+jm0xE25oETfG39uAmmPtIboBdB1QNtBziuoSqKqBFTLobyE5jM7LRgAVQsbnUXY8zNhjUcw+Oupha1nz/VcuDKpCWphIBYdS73OYtMBXwzw3GdntOJUnH0P0XYQbQ214CDTqiPoRkDtE5qLAqpV0FP21RN2rNhc0HsZgV6MNlEFxtSbYZt+uVS/fpnY4o1xtWPSYUwyC/tNLf6wbmoziYFpCmDDZ01tfqn5CT/HxhB79tTzlqgpY+3HnnFTSQ+RTSuQ6oQ11R5M4QGbXkUswc+djTjO2ANIYUzEb0ylVZ65sW1KqXctxril3plU+bBcyVoLKTK2EtcEKMXxICsFSAnTKCxubLA4KaEWhEklMOk0xLxjic45iTcYVIu586YQ6GJWs2NWuABWgU9rDgHWdRD7C8hKgWoGvWZXwijJvm+eNSanCuJ/tC/A54BLYFNzQrUPq+6kAfSEZtWpmvc8B70GtR1g9PJ9de+s/9zW4jU0wCEHevstlJOoYRPWCg5srecCpuY4okKD/Qnn6akZo5cH6KUWTWqxpBZlqMpL1YupkWJ1/UWbA5sQLMI2Y22PbS6lUmJ4LQdcORVh2G+McgCbKzsGZmOAVQJgh5FQxtr1wQ5YmsG7MExKQTQNUClQpSC0YYOJruN/dhMlL57hAckPWJX+RgAjJl2sLfGNaRpi73eqjTEmaYx5zUn3KSIDGDlI3UOKHoA3bsWg10+tH1kD6KmCmdVQewuWctoOUGaZwLXteFMPgC8WeiwXvDoLeLF7WgNagizwKQssdSNhlEA/s/0rQC5WAQ72DE9qGs7x1AKo5mys4hzPnWWoWhhrvNKCugDwItFXyLPmHJg8Y0BSQvQ9SEiISkFeVlACkK0G0KCzMTjNnGN8Dn6EPUCXN1fNvzxAL6R1NuWUZJVqd526MfBLAWYO+MKxr/N8Kekox32nGIfchpIDL3c/BUYpiTVWJgZ24ZymfscSKTRCqZiXy2ZXTdFDq8CDGa6tGfyk4bMhIUCTmrNZTyuQFBDaQO4uIOYLoO1Abcum8oZsnMOE5LfGM61OzSHO93IMn18mRinAjPVRMo4NaGUzBoZs3U7VXM3Znl5o64itiS05ew0yxobgqpZAN6T9iVvYHu4MzyyTyDopUdp3QBuIvuezxa6HnAvUF6VNH2ST6dEyuolzQJcdvDBkLNVVC8PuCJd7yN7w+aUD6U4P7yX63kq8nloz8z4OBjj2vJoACLSgOa8TpQ1o1vD0dRX6mYJprNN6tcwKoRcje3WGXh6gl1MJ+tfGwCKnxgv7y3GfY+odv/2YGi9WrmRMuT5yqtawfgqASyUzn0IgKp3/MUBMSathH6mxj6k4Vy7Hfaq4q6XFXag6E8q25eU8GwBvNgXNJqCm4oSZtQIJwDQcKR8AaiWhlATqiveotgVMvwQ8z0BAxAxeNqWSd8Q+4wGKXctJ7TFtSEqjcEi1c5acKT3AjEUt+DepJJ/l7TmrSM3m+dbcfiVxKo2fpzqKuSekzlqjFp1+9nRHhsGO2hZCSYiOVZCym0HoKdoTCt2OHKQ8p7rkNlmCUp21ytzTbJk5Z2lWdD2W53QW2KwWIuaesBK67cBU06qmAoBYLPhvr3nc3QRyUUHNa+iJAgQYvAUAIaDb69llYQxUctdjHKr7nJJ2wmsptWJss4+pOWP9jW0mJdJMbKz+OFKqTL/tUsmoVL1YoqKKjTunRgsZkDEpdVPVbIKGjSps321IDvCscYRobP6wnSnMsQmrz2a8uZJd2IJYndTfwByvumDsmR87TwljwS1I2bIEwUDNmZn3rMN57vOygXHmIcYElkiCuTIbAmCRVOVASMrhjIkBj030xbzzzk+XUjys5Se63kZsWa6lmBFLbEy5mKvBgyx9ON09rZcA0veAVBBNDdX1mLY91GIH6lQFXXtqVKva5Fx1BNkx4KndFnJvAdFa/7uuW3mewUo1kPD4lgeObnyDb+lSOgXA1q9ELDEDfMbX9xDGQHY15LyHqhVrN1wKIyUhF9ezy0KJtJWjFJikuNqYJJaT0GKLP7wXkzLDNnMSbHgtBhY5iTJUQaZAvwQsNlFR5UAxNp4YWPtjDaXFWF9HJDEMm5E/R06lqdgYwmWGFtMJoBRoUsPsNDC1BFUCpnEqKDBHC/aX0pCQswpqN5xfG/5K2BxmgcS3csa3gdpyrfO90vnMSeipNg9bZqX4QXW0/QI2AjJLic2wFa0wS2dv2RmoeQ8575dSjwM7sQQduE08IuWkHNNzc539Hdw7ZyQIDCqDn6AyEEqDjGY/uF6j6TTU3hRmxpnJqeI5HKK49AZyoSH32bDEWWYObgjOD88Bl59RIaVpcPObexetupO6ng2BDAHacP8VBwpwganhsrPrzXMLXfugF1JK8gKKVQ9RSnGq4UJOSVXuXmqTTnHAPuWAPFU3lNpSwBLrp1D9N0qbqBZzzESs3FjZK6AaS1ppArxAHeBVFUTTgKYTzh0244SZnHgTMLUABJ9ZGMUWa6jZn0q1EmZSQ9UVH/obAdDS72oAPmDFiGBFvZRgKmIWg+H1Ihpjivz5iWk/wvGlJPdDUPKZhGR1pgSrNvsekFM+a9WE6sICsqv5jHWv5fiSLvqKPY9lqcfGwBwijSxVnaHBSs5Hr5hCLZX/HA6AjOHzPecaYAyq/QVo0qCa1qDayz6uic/qWlbdis66HzhrTAt4gypzeIAE4PmWxOEz+gyIrbOintcM1NS1g/Qs7HqCzRsoqMWm9PIDvZS60lHsZUmp0UrUcjkONiWxxdQ94VhzklmqnF8+/JwDY39cYdmQ/PGPbU6l6tEYjTEBKQmjVDW3BghmI6qE7culsYpQcjBYodkE5oYp+uMN9ERCT+XSIbhagp+pxOAXpRtATyXUTg05b3gjcpuYWj6DsM9DduNwcSDdeFK+e0VAVyqZpRhNv53YvXVUqUdEK7/fyntv56lnaU0YAzHXkHO7wXY9n+dpDUwaUM3bp9C2TtuBEhFZwu8xY5bUvaQ6NlwjTh3qgEMpHqsQ/H4QAQsBsWgh9qqli4zTFvjO9d4zUAh4gQ8eORcbN4bYu+SP0y/rbrs2tV4G81aKJT4lQVaDApt1ncwW9A5STh2Z+j5WNgVgfn9+udhm4HO6sTGPqTb9flNtlUh9qfZyIJWa0xK1ZziGsO9QIgsZk1w/m0gFG6jHYqlgVtpyas0hZxibYtNsAn1qB4ubpmhPKj5TEZyWRS2IQU8BuhYcEaPn77JjS02qbJvKLnwl7cajlmclGiv+TyuUsCIMn+tQ85RjenL3w3tjIHsEEnvyXM9aQKLtIOYtg5qTRGyILz9fnHAg0XbsqhCARaxfRyUq5KyVZ2zdh+VhAaTv+f1xAKINB9CUljFzMTF7K8GGQRJ0HPCihipuDCkty8q41Wo9C5gksUzlpPXAzK34tW4lPeSBYkytlwOosGyqn9y1dSXIEIRyz5MqlwLsFKDkKMfJp57Nfxb/eo7TT403N9aUGvSIaV01FFtqSo7duDNBe+MUe6cr7N/CIaDUApi8yOGdSAH9RMA0gFECJMmLdM/ZsLlRASElhpFozVFBDLG5uhDwRzmoOAuc1o+MSpmUGKOTqxOWO/QwfQAyw7mYQMe/276EMA2fwTor3CHcmADmi8GnjxaLQcoLVZthX+uMy7+WNb5J7W0WQJz05AxGhig/ogcdsACl5bOSse+WBfOUGjOUnP3POSY6fFb33Rs3j3PVUAdSgg6RRfblA3qOUqrAdTf9WNlYHzlVX9h+yNGG/aTAKwdoMYBNqVDH5iY1B+HYY+OKMRCxujnuPiflptSdMWZkjY2x1GgjGSUjlPLc2YOQy01SsOTWHRdY3ETQU0J1SUB2AgCbkFMF9FPBICaElfLsFEgBaiqgryEAiEXLG5iQANmNQcnVDcuX9mIhy1CwmYaUY0L8DS72DuRoTEoc2zw3pFXrSLehC3tmNweIIJoGwqYTJykgjK3j/OF6G2i565YMxgjgrc1AbRJFx82ZF7llBUQc8xSGvXPkS60psHN9DP6JMr1+Y/dzDJiXXYR8Vk6ygZF77zehlwfopaQwn3ILsBQocsA2tkhT4yqRUFNSW+p77HlDAIrdX0cKGAP2MZDKtRm2X1LX73MNKgnAPJruxXdPgNtUvLMNYyB6T2IDAAn0x8DSWw8O/quAZbj+ZdBfPZEQumZlkLB5p3sNARv8GNr6bRkIqJVQUDl/qZQJfZJSDFzsXoypK5HmSvo6IooldoWxaj7rs8c/BUfLEb0HBERAu2ApaIhGkp7nowzuXcysBMA3XHZnfj7g+XFgIyHElvc89aX/t2zg65VN/O5EBNKbz+fLA/RS4v2Y5JVrJ7Zo/bbCMv7fks03lO5iY809V45jykll4fVc+TF1YozTT42jRD2ZkhJyUkNQfiyvXYrIcq6x+kl/KSEPOgj7Zu/uLM5Os+wBtceGKmrOxiv9zAb/FYCeWKCT/N1UDHgkGURJCiglIKVggwohWEXVAxymAoA04IRkYiVY9RCkOhMhJJX2xr9XMJHxd6fkNyy9fhWItAZay4MYYv9K4aQ8zqsXBbxgbo8C8KLgnLgfqcx/w7yMwODmAGBpQJI6k/SfYRPBIlp8A7Vt5DnWpWsf9MZUcynwyNWNtbUOrdtfCLb+phEDw7C9FADGOO7cmEs48RQQ5QAu9z3WXgkQJsZDiUVRtPkIGS2TzHXmV7UbonCBo526024iLjXL5DzQ7wjoCdAfI06T4poVNsbgcbbk1DOB6jKhGeIMLperBLgPKezG7DYvQAha+u+Fm5gzegmeyzelP5RKrlSSL3nfDgN4hRvwgeciyzwIe240NzaslzWfJxoAbzD8SABeKa1r1BIGrS6W+mIUUyPG1mshkdYrY/PHG9Khgp9vONfAywH0SgEmBoA5FWEJQOXKxe6V/lC5ftd5jpjKM0Vj0mx43a+zjsSX6ysHhCVky+VMwuPVjkD95MVqHKKnWEtOYQxky0kyZQuIRoB2+LBeTwnGrUICR8PXHGVedmzYAgGQ4ASgJKzxhJKQ8w6YSwjJIadWjFjcob/L5hCxtosFOF5rHmLMSOnvdQUlOP49129/FUzM0umbWjuPLF2DrHFHAHabpmwqLXcoX8qDjeX3pkMAuP932dya58cpOgTYObr2QW+MxqQ9R+uAZ1i+FPhigFTSfm6cqc0mJSmN1Qn7ydXPPW+JuiolzcaeLzauBK27IYyVH0vxshJjcwi5BAa/AfjIZob2HNIJoIqgJ2BjFkXLJJ4dYBrYsEts8an3FWolYGoJNdeolORo+vssVQqbmJPA0SoEsLQoXH2g6Pu1rkl9VFIbk8yXna0PlIV0GEBYkais1AIjV6wJASQBL6UePgqK/T5hv7Fyfv0DLg9HACKl481dj40/vDd6tl5IL1/Qy0kcKRWhXy+mFsxJjqWq0rFyY8CQK18imYbXY4CWAtYYdxirGwOqnPQ3Bn4lYHtEtPaG5Rag55sHxf55UBLU1DBNBVPLAfhMDagFg59WYCd1gDdWxZKfmRFEy24MegfQU4HmIqAbieYywTQMfnUj+ZxvXy61pEQgsOrNGWMc8N1LvIexqCGjlAKwUuZozd/1KEGl5BxTSIMxC/m1pLANQD6WimjZXFqCT10/rOSVqx8DrfBa6j3L+iYi/Wzr0MsX9HzKAVSM4ymRvGLfN1EZjNUbUznG2gg3HffsMak3B3J+vfBerJ/UOEs2wdT9I5YC1qVsNAxHzmDEOo+LuuaIHZMGZmcCfbxBf0yhn0r0UwE9ERAEqA4sBdaAnhCoJlBjIBrDPtCNhNECQguYiuNzVhNOpqnmAnXlVJ8ClRDs7QBgcKAmWm7WvooziJoRng2tDSqp3z9FYxqH0epHawkZa7PkTCpWdi0JuYBKQpeFUl84NlcmrDNGuefOnm9Hxpw7N86VD59j+HwI7FtrN3n/+98/qFHcv7vvvnu4P5/P8eCDD+Kmm27C8ePH8cADD+D5559faeOZZ57BW97yFuzs7ODWW2/Fj/3Yj6F3B8KbkOMU/X8hxc6dUm3F6qbuxepdqQ3aAVDpBpFSX8RUUeH1sGwCrIu4rk3n4wpId+su9Gx531gFWPrmSU4dRDtT9Dcew+LWGfZum2D3tgp7pyX2bxWY3wQsTrHlpkvpIjUgtAAqgmo0hCSI2kAe74DjHfSOQXvKYHEjYX6jQHtSYH5KcLqY4xW6ExOYWQ2aWvN6accSe4aMdWqxlFeifs+ts7E1s4nabdMjjGTx1Y02/BeWiX0/LI1JcTFwiAHIYfpPPWMKjGNAG5PYcufIsXY3ZsoCWlvS+47v+A78+q//+rKBatnE3/t7fw+/8iu/gl/8xV/EyZMn8a53vQt/42/8Dfz2b/82AEBrjbe85S04c+YM/sf/+B947rnn8Df/5t9EXdf4Z//sn232BGPqx/CzT2Oqw1gfqfuxdlLndWG9knHFpNPw+pj6cQz8U8AXqy8knxeF4z0qBuAKMA8lC6VYBWToIKBIYeNtKs7F1ki0JxQWJwXaEwL9DtDvEEt1E2JXBiOgFvZ8z+oihSBUDc+tMRKQBNMYGMHph0wNmIlAtcsSIyBt0OopaiEgNSc2FUQgozhyvdZLac9Nree+sLE68+AExj+X1t9Q5Znt7wqRrxYtUWlu6k4T9pn6nlOB+uONXYu1E4JS7tytpH+/XNhGbl5yfW5Ca4NeVVU4c+bMgesXLlzAv/t3/w7/4T/8B/ylv/SXAAAf+tCH8Kf+1J/Cpz71KbzhDW/Ar/3ar+ELX/gCfv3Xfx2nT5/Gn/7Tfxr/5J/8E/z4j/843v/+96Npms2eokQd6d9PgdTYWVuKUqBawg2H3zdtK6ZKTKl0Y+eYOaYgpZ4c++6PcZ0NKSLlpRZG7jo3tb7VZpF6Kqn25HxfJCVMJWEqgX4q0E+B7jhBHzegxkBOewjFSNfPFdALRj0CdC+has02MMTBsaAIBJ5HXRNICmvZyX6ADTg4tVxUEF3NwamVslkDBNh6Jv0OxwwFkvNQqmkoAcaw7JUErg3aHtuQi0FMxN1hjmI8Y9bKKbDKqUnDdsbGHgJqDExj4JlqO3fWeth5XPst+OIXv4jbb78d3/It34K3ve1teOaZZwAAjz/+OLquw3333TeUvfvuu3HnnXfiscceAwA89thj+M7v/E6cPn16KHP//ffj4sWL+PznP7/ZE4xJTynpJ9bG2BlVrs9U3VCyWgc8S7npmPTmc8wpIPTVpTnVcOz8s5Rh2ETlFBlH6kU/kg0pQeuoUVaiW9iEl7ph0DMNoGcEPTOgykDOekxmHW4+tYszN13AiZsuoz7RQt3Q8XmeltCdQreooHsJ0hIQYKd1SaCJgT6h0d1osLiJ0J4SaI8J9DMJvVOxmrNSQwLbFZLpJR/z2Rul2PvBjZXVLym7iarziNo6anXlYZ8lx6StM1b/HDcmAYZMUIoR2lTVWwKi/t+jNGZZS9K799578eEPfxivetWr8Nxzz+EDH/gAvud7vgdPPfUUzp07h6ZpcOrUqZU6p0+fxrlz5wAA586dWwE8d9/dS9FiscDCy5R78eLF5c2Uim7kPOrA95QEuI7UFRvHmCoyJ3mmKAbiqeeNnY2FffufSzarUJ25Dldfev8KcfwpK7sUl3tggSXGRTZsFYg4XBUAstkTQPxP9ALUCJARrMKUBrXSuGG6QKU02r7CYlEBJKC1ZPUncXkAgCSgIchGQyq2KuymDURfQ3YCrZbsBL+voOqKswUIARKSHdaBVWnPmuPH3rtiwM8xiLH3okS1mevDL+vulb6/6wBOpK0jsRq9Cu917HrsjC8mATpaS3V7FShl1LIJrQV6b37zm4fPr3nNa3Dvvffirrvuwi/8wi9gNpsdaiA5euihh/CBD3xgvUrrclQhADnKnQfGwDF1fpY6e0udSfrfU2Nw90qfJTbG2D1/A8lJ0ldQJVWyMFP13EKNqUhKrodjWCE3L/ZMbwA7gOM22rM00Rs+S5M2/JiNvNJVApgBXaew6Cu0WoFIoO0rGCMwm3YwJND3Cu2iAvUCEARREUgL1mb2EkIaKElQsx56VkFPOC2RqYTNyC5XpE/fMf1A9oWRuVyLNlVVrqsCj4HtpgxWYdkrvfmnztaOoi2fcmdzObXoOuvwsLTuccY6dKjd6tSpU/j2b/92fOlLX8KZM2fQti3Onz+/Uub5558fzgDPnDlzwJrTfY+dEzp673vfiwsXLgz/nn322dUCMRViKcfoU4ozDdsolW5SUuiYQUlYxwe/MSBKXUupO8PvJeOJ0Zjqdk0K1Sa5c7tcG7kzgTGjgNj1A2MIo9QbYmmv7SEXBrIHQBxfU7YCai5B+xV0r3Dx8hTnL83w4sUd7F2eoF3U0EZi1nSYNh2kJAY9LRjwJHFUMSOgW4murWBaBdHzWaDQ3JeIhB6LjpMfLjt/a1NOKkvdj10fY9g2ocO2eZRq1mjzm59dpcCr5FggJ9GVrEOfBlXoyFyVrN1Y+aMA3EOB3u7uLr785S/jtttuw+tf/3rUdY1HH310uP/000/jmWeewdmzZwEAZ8+exZNPPokXXnhhKPPII4/gxIkTuOeee5L9TCYTnDhxYuUfgFUA8r872oTzKzmDSoGZXyZ1njcm1fltxqTDkoUbA8ZY/cLztANtpO4dQuIbWwQxc+fcmUNYJtaeXy/V1wHgDOaAXJ4xY5bzazNpV/sawhCEwZAlnRSxmlML6F7BaAXSAtRLGCPQ9xLzroIhgbrpeYWSAHoJWiiglQABWCiY3Ro0VxAECGMjuWiC6Hh85BKEOrJpjwapT4rob3bYM5NIg+NlYir7WBuH0Socts0rpJo8SgrBL3zH/e/hmV5qPWw0BiGz7a0LXtGjhg1pLfXm3//7fx/f+73fi7vuugv/9//+X7zvfe+DUgpvfetbcfLkSbzjHe/Ae97zHtx44404ceIEfviHfxhnz57FG97wBgDAm970Jtxzzz34gR/4AfzMz/wMzp07h5/8yZ/Egw8+iMlksv7oyWAIYb9yDXEQ8T8fhWSS4xRj3K3fZ07SiwHjGFiNjSdsu/R6rNy6qqhoM+s5vcZULSnASp3Z+ddSUmB4sB/+Tc89DVLekNZHW0nPuRnUbMyCiUEz7VHXmuMXtxVIEVSlUVVmUJfWlUY/69BdroFOQhgBUgT0AmJh8/BJAMb6+vUE2RG7MRBxZm+fHDgP32kVwBNqryuizjrMOfA3Ah3BGriSlNNm5M7xgMP95kd59pZr+zC0Fuj90R/9Ed761rfia1/7Gm655Rb8hb/wF/CpT30Kt9xyCwDgZ3/2ZyGlxAMPPIDFYoH7778fP/dzPzfUV0rhox/9KN75znfi7NmzOHbsGN7+9rfjp37qp47kYbKbfYyTzElewEHJJmZ8Mkalkl2qfM5oJPY5B4ixs7rw+lj7/pg2oBy3VnovJon518O/61iXHWZhERGEIU7Tog3EQtsg0wSxY1WQJFDtdLjl5C5unO2h1Qpf2zuG/baGEISm6lErgxsmC8yqDl+dHMPXcBzdbgMsBERnn01zKDOhgeqygFqwRAkD7lv7AEyedCdXQ5J5v2/qXGfzCQneyZQW5qUGkNL+NxnnET/bUTAhKUYvdX+ksZd8TtYlQckESt+4dPHiRZw8eRL/n/g+VKKOFyo1QEnROsB2lP3l2gHSL0wMvFJtp4A1RrkXtPDlTZlFJwrHr0uxVgqXdQ7exwwIouMWcvDVGzKmK8XO6ZMJxHQCc/IY5rffgL1bKixOCXQ3AIubDOQ37eF1d/wR7pi9iMt6gi9fvBkX2wmIBGZ1h4nqcdvORdxQzfEHl2/CH774Clw6vwNxvobaZ/CUPZ8RVvuAWgBqn1MQTc5rNOcXUBf2IXb3QW0LtJ3N9M3Bp4clH0h6/twdGaXOyTc5X9+k33XaiZ21H3Zzvkob/FFL5CVGNdE+ryQD4VFPHT5hfgkXLlxYHncV0ss39uaYCnDT+jEaA411+gvP8GLtxMr4n2OAF/YRk/ZiNHbGF72cyKdV8pI7cPNBbsxtIDLHoWS4DgjGfIOy5Z0FpzGAEAwuVtqSrUG1IOg5Z1aQrUDfVrjYTnG+2sHlvkFr2IJTSYNGauxULSphcKKa45bJLr423cElMYPoAbXP0p1wP6UEXKZ1kgAJsJTnVJsJYItdD6XmI9tEw/er5D1YZ0OMaSRixlmlzF0JM7juuK4Qlf5OY+XGpL2kVmST33adcleArknQc5xqT126UAoQjqq8X890q99ToBNrN6mezLQzfNfLukgsWArCw7t+3Jj9cil1aU6yjJAgNs8fErwNn+VBADvQ7rL8AWyLVfWlv8j8rgAYJQDvQAICC5LRDh3JlQEJYksVQYoBryPQPkCXAJpNAKEA4qDQMAa/392Ar55gt4VOK2gtIKXE5UZhUjXYn2mIWY1Wt9CXF6D5PnRrUF2WkAsM4cioB0QLkCbIOcEsevTdHLLbh+gXIN0CuuMwZEQgd6ZHEdAjWvntkkqgdd+J8P0skaZKrq+sK+/9Dd/5oa4+2IbfLiEY20hqhZJxlrRxGNLB3wgJKUB6BBgPM8xNn3GEucgBtdv7N1FUXpPqzf/zf/4P/uSf/JMv9TC2tKUtbWlLLyE9++yz+BN/4k+sVeealPRuvPFGAJyx4eTJky/xaL7x6eLFi7jjjjvw7LPPrq3/vt5oO1fltJ2rctrOVTmVzBUR4dKlS7j99tvXbv+aBD1p4weePHly+wKtQSs+jlvK0nauymk7V+W0natyGpurTQWel9BOeEtb2tKWtrSlq0tb0NvSlra0pS1dN3RNgt5kMsH73ve+zaK4XIe0na9y2s5VOW3nqpy2c1VOV3qurknrzS1taUtb2tKWNqFrUtLb0pa2tKUtbWkT2oLelra0pS1t6bqhLehtaUtb2tKWrhvagt6WtrSlLW3puqFrEvQ++MEP4pu/+ZsxnU5x77334jOf+cxLPaSrTr/5m7+J7/3e78Xtt98OIQR++Zd/eeU+EeEf/+N/jNtuuw2z2Qz33XcfvvjFL66U+frXv463ve1tOHHiBE6dOoV3vOMd2N3dvYpPcXXooYcewp/9s38WN9xwA2699VZ83/d9H55++umVMvP5HA8++CBuuukmHD9+HA888ACef/75lTLPPPMM3vKWt2BnZwe33norfuzHfgx931/NR7ni9PM///N4zWteMzgGnz17Fr/6q7863N/OU5p++qd/GkII/MiP/MhwbTtfTO9///shhFj5d/fddw/3r+o80TVGDz/8MDVNQ//+3/97+vznP09/+2//bTp16hQ9//zzL/XQrip97GMfo3/4D/8h/dIv/RIBoI985CMr93/6p3+aTp48Sb/8y79M//N//k/6q3/1r9IrX/lK2t/fH8r85b/8l+m1r30tfepTn6L//t//O33rt34rvfWtb73KT3Ll6f7776cPfehD9NRTT9ETTzxBf+Wv/BW68847aXd3dyjzQz/0Q3THHXfQo48+Sr/zO79Db3jDG+jP/bk/N9zv+55e/epX03333Uef+9zn6GMf+xjdfPPN9N73vveleKQrRv/lv/wX+pVf+RX6/d//fXr66afpH/yDf0B1XdNTTz1FRNt5StFnPvMZ+uZv/mZ6zWteQ+9+97uH69v5Ynrf+95H3/Ed30HPPffc8O+rX/3qcP9qztM1B3rf/d3fTQ8++ODwXWtNt99+Oz300EMv4aheWgpBzxhDZ86coX/+z//5cO38+fM0mUzoP/7H/0hERF/4whcIAH32s58dyvzqr/4qCSHoj//4j6/a2F8KeuGFFwgAffKTnyQinpu6rukXf/EXhzL/63/9LwJAjz32GBExkyGlpHPnzg1lfv7nf55OnDhBi8Xi6j7AVaZXvOIV9G//7b/dzlOCLl26RN/2bd9GjzzyCP3Fv/gXB9DbzteS3ve+99FrX/va6L2rPU/XlHqzbVs8/vjjuO+++4ZrUkrcd999eOyxx17CkX1j0Ve+8hWcO3duZZ5OnjyJe++9d5inxx57DKdOncJ3fdd3DWXuu+8+SCnx6U9/+qqP+WrShQsXACwDlz/++OPoum5lvu6++27ceeedK/P1nd/5nTh9+vRQ5v7778fFixfx+c9//iqO/uqR1hoPP/wwLl++jLNnz27nKUEPPvgg3vKWt6zMC7B9r0L64he/iNtvvx3f8i3fgre97W145plnAFz9ebqmAk7/v//3/6C1XnlwADh9+jT+9//+3y/RqL7x6Ny5cwAQnSd379y5c7j11ltX7ldVhRtvvHEo83IkYwx+5Ed+BH/+z/95vPrVrwbAc9E0DU6dOrVSNpyv2Hy6ey8nevLJJ3H27FnM53McP34cH/nIR3DPPffgiSee2M5TQA8//DB+93d/F5/97GcP3Nu+V0u699578eEPfxivetWr8Nxzz+EDH/gAvud7vgdPPfXUVZ+nawr0trSlw9KDDz6Ip556Cr/1W7/1Ug/lG5Ze9apX4YknnsCFCxfwn/7Tf8Lb3/52fPKTn3yph/UNR88++yze/e5345FHHsF0On2ph/MNTW9+85uHz695zWtw77334q677sIv/MIvYDabXdWxXFPqzZtvvhlKqQNWPc8//zzOnDnzEo3qG4/cXOTm6cyZM3jhhRdW7vd9j69//esv27l817vehY9+9KP4jd/4jZXEk2fOnEHbtjh//vxK+XC+YvPp7r2cqGkafOu3fite//rX46GHHsJrX/ta/Mt/+S+38xTQ448/jhdeeAGve93rUFUVqqrCJz/5Sfyrf/WvUFUVTp8+vZ2vBJ06dQrf/u3fji996UtX/b26pkCvaRq8/vWvx6OPPjpcM8bg0UcfxdmzZ1/CkX1j0Stf+UqcOXNmZZ4uXryIT3/608M8nT17FufPn8fjjz8+lPn4xz8OYwzuvffeqz7mK0lEhHe96134yEc+go9//ON45StfuXL/9a9/Peq6Xpmvp59+Gs8888zKfD355JMrjMIjjzyCEydO4J577rk6D/ISkTEGi8ViO08BvfGNb8STTz6JJ554Yvj3Xd/1XXjb2942fN7OV5x2d3fx5S9/GbfddtvVf6/WNsN5ienhhx+myWRCH/7wh+kLX/gC/Z2/83fo1KlTK1Y91wNdunSJPve5z9HnPvc5AkD/4l/8C/rc5z5Hf/iHf0hE7LJw6tQp+s//+T/T7/3e79Ff+2t/Leqy8Gf+zJ+hT3/60/Rbv/Vb9G3f9m0vS5eFd77znXTy5En6xCc+sWIyvbe3N5T5oR/6Ibrzzjvp4x//OP3O7/wOnT17ls6ePTvcdybTb3rTm+iJJ56g//pf/yvdcsstLzvT8p/4iZ+gT37yk/SVr3yFfu/3fo9+4id+goQQ9Gu/9mtEtJ2nMfKtN4m28+XoR3/0R+kTn/gEfeUrX6Hf/u3fpvvuu49uvvlmeuGFF4jo6s7TNQd6RET/+l//a7rzzjupaRr67u/+bvrUpz71Ug/pqtNv/MZvEIAD/97+9rcTEbst/KN/9I/o9OnTNJlM6I1vfCM9/fTTK2187Wtfo7e+9a10/PhxOnHiBP3gD/4gXbp06SV4mitLsXkCQB/60IeGMvv7+/R3/+7fpVe84hW0s7NDf/2v/3V67rnnVtr5gz/4A3rzm99Ms9mMbr75ZvrRH/1R6rruKj/NlaW/9bf+Ft11113UNA3dcsst9MY3vnEAPKLtPI1RCHrb+WL6/u//frrtttuoaRr6pm/6Jvr+7/9++tKXvjTcv5rztE0ttKUtbWlLW7pu6Jo609vSlra0pS1t6TC0Bb0tbWlLW9rSdUNb0NvSlra0pS1dN7QFvS1taUtb2tJ1Q1vQ29KWtrSlLV03tAW9LW1pS1va0nVDW9Db0pa2tKUtXTe0Bb0tbWlLW9rSdUNb0NvSlra0pS1dN7QFvS1taUtb2tJ1Q1vQ29KWtrSlLV03tAW9LW1pS1va0nVD/z8PaHihP3vehQAAAABJRU5ErkJggg==",
                        "text/plain": [
                            "<Figure size 500x500 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "i = 2\n",
                "\n",
                "plt.figure(figsize=(5, 5))\n",
                "plt.imshow(train_x[i])\n",
                "plt.title(f\"Cell Count: {train_y[i]}\")\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "id": "Pv3Z8t5DHCt8"
            },
            "source": [
                "Now let's prepare the data for the model.\n",
                "\n",
                "PyTorch based CNN Models require that images be in the shape of (C, H, W), wherein 'C' is the number of input channels, 'H' is the height of the image and 'W' is the width of the image. So we will reshape the data."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 21,
            "metadata": {
                "id": "KF9Wh43H6agK"
            },
            "outputs": [],
            "source": [
                "train_x = np.array(train_x.reshape(-1, 512, 512, 1), dtype=np.float32)\n",
                "train_y = np.array(train_y.reshape(-1), dtype=np.float32)\n",
                "\n",
                "val_x = np.array(val_x.reshape(-1, 512, 512, 1), dtype=np.float32)\n",
                "val_y = np.array(val_y.reshape(-1), dtype=np.float32)\n",
                "\n",
                "test_x = np.array(test_x.reshape(-1, 512, 512, 1), dtype=np.float32)\n",
                "test_y = np.array(test_y.reshape(-1), dtype=np.float32)\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {
                "id": "AAtv0pMOVmeO"
            },
            "outputs": [],
            "source": [
                "train_data = NumpyDataset(train_x, train_y)\n",
                "val_data = NumpyDataset(val_x, val_y)\n",
                "test_data = NumpyDataset(test_x, test_y)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Creating and training our model"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "id": "E4MBSZBELCQF"
            },
            "source": [
                "We will use the `rms_score` metric for our Validation Callback in order to monitor the performance of the model during training.\n",
                "\n",
                "For more information on how to use callbacks, refer to this tutorial on [Advanced Model Training](https://deepchem.io/tutorials/advanced-model-training/)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "id": "jIrM3hYhHIvK"
            },
            "source": [
                "We will use the CNN model from the deepchem package. Since cell counting is a relational problem, we will use the `regression` mode.\n",
                "\n",
                "We will use a 2D CNN model, with 6 hidden layers of the following sizes [32, 64, 128, 128, 64, 32] and a kernel size of 3 across all the filters, you can modify both the kernel size and the number of filters per layer. We have also used average pooling made residual connections and added dropout layers between subsequent layers in order to improve performance. Feel free to experiment with various models."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "metadata": {
                "colab": {
                    "base_uri": "https://localhost:8080/"
                },
                "id": "EkX98ssfVq-L",
                "outputId": "b545ff7f-46ad-46b3-eae4-20141e95cd20"
            },
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/lazy.py:180: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.\n",
                        "  warnings.warn('Lazy modules are a new feature under heavy development '\n"
                    ]
                },
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Step 10 validation: rms_score=43.5554\n",
                        "Step 20 validation: rms_score=61.2546\n",
                        "Step 30 validation: rms_score=39.8772\n",
                        "Step 40 validation: rms_score=52.2475\n",
                        "Step 50 validation: rms_score=42.5802\n",
                        "Step 60 validation: rms_score=38.1957\n",
                        "Step 70 validation: rms_score=72.0341\n",
                        "Step 80 validation: rms_score=35.5798\n",
                        "Step 90 validation: rms_score=46.5774\n",
                        "Step 100 validation: rms_score=31.5153\n",
                        "Step 110 validation: rms_score=38.8215\n",
                        "Step 120 validation: rms_score=35.6907\n",
                        "Step 130 validation: rms_score=29.9797\n",
                        "Step 140 validation: rms_score=28.0428\n",
                        "Step 150 validation: rms_score=44.3926\n",
                        "Step 160 validation: rms_score=37.7657\n",
                        "Step 170 validation: rms_score=34.5076\n",
                        "Step 180 validation: rms_score=26.8319\n",
                        "Step 190 validation: rms_score=26.6618\n",
                        "Step 200 validation: rms_score=26.4627\n"
                    ]
                }
            ],
            "source": [
                "regression_metric = dc.metrics.Metric(dc.metrics.rms_score)\n",
                "\n",
                "model = CNN(n_tasks=1, n_features=1, dims=2, layer_filters = [32, 64, 128, 128, 64, 32], kernel_size=3, learning_rate=5e-4,\n",
                "            mode='regression', padding='same', batch_size=4, residual=True, dropouts=0.1, pool_type='average')\n",
                "\n",
                "callback = dc.models.ValidationCallback(val_data, 10, [regression_metric])\n",
                "\n",
                "avg_loss = model.fit(train_data, nb_epoch=20, callbacks=callback)\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Evaluating the performance of our model"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Now let's use `mean_absolute_error` as our test metric and print out the results of our model. We have also created a graph of True vs Predicted values in order to visualize our model's performance \n",
                "\n",
                "We can see that the model performs fairly well with a test loss of about 14.6. This means that on average, the predicted number of cells for a sample image is off by 14.6 cells when compared to the ground truth. Although this seems like a very high value for test loss, we will see that a difference of about 15 cells is actually not bad for this particular task. "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 32,
            "metadata": {
                "colab": {
                    "base_uri": "https://localhost:8080/",
                    "height": 443
                },
                "id": "-Iyg97wDV2JW",
                "outputId": "6c024507-9cd5-45a2-8e2a-553af42cac44"
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Train loss:  19.05\n",
                        "Val Loss:  22.2\n",
                        "Test Loss:  14.6\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 400x400 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "test_metric = dc.metrics.Metric(dc.metrics.mean_absolute_error)\n",
                "\n",
                "preds = np.array(model.predict(train_data), dtype=np.uint32)\n",
                "print(\"Train loss: \", test_metric.compute_metric(train_y, preds))\n",
                "\n",
                "preds = np.array(model.predict(val_data), dtype=np.uint32)\n",
                "print(\"Val Loss: \", test_metric.compute_metric(val_y, preds))\n",
                "\n",
                "preds = np.array(model.predict(test_data), dtype=np.uint32)\n",
                "print(\"Test Loss: \", test_metric.compute_metric(test_y, preds))\n",
                "\n",
                "plt.figure(figsize=(4, 4))\n",
                "plt.title(\"True vs. Predicted\")\n",
                "plt.plot(test_y, color='red', label='true')\n",
                "plt.plot(preds, color='blue', label='preds')\n",
                "plt.legend()\n",
                "plt.show()\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Let us print out the mean cell count of our predictions and compare them with the ground truth. We will also print out the maximum difference between the ground truth and the prediction from the test set. "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 35,
            "metadata": {
                "colab": {
                    "base_uri": "https://localhost:8080/"
                },
                "id": "6CLFMtV9PzrE",
                "outputId": "b693fd0d-e208-4517-d266-da36643ca79e"
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Mean of True Values: 87.60\n",
                        "Mean of Predictions: 87.80\n",
                        "Max of Difference: 31.0\n"
                    ]
                }
            ],
            "source": [
                "print(f\"Mean of True Values: {np.mean(test_y):.2f}\")\n",
                "print(f\"Mean of Predictions: {np.mean(preds):.2f}\")\n",
                "\n",
                "diff = []\n",
                "for i in range(len(test_y)):\n",
                "  diff.append(abs(test_y[i] - preds[i]))\n",
                "\n",
                "print(f\"Max of Difference: {np.max(diff)}\")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "We can observe that the averages of our predictions and the ground truth are very close with a difference of just 0.20. Although we see a maximum difference of 31 cells between the prediction and true value, when we take into account the `Test Loss`, the close proximity of the means of predictions and the true labels, and the small size of our test set, we can say that our model performs fairly well. "
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "id": "oRJQNvpuM4m6"
            },
            "source": [
                "# Congratulations! Time to join the Community!"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "id": "DLfdVtsVM7gc"
            },
            "source": [
                "Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with DeepChem, we encourage you to finish the rest of the tutorials in this series. You can also help the DeepChem community in the following ways:"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "id": "7pq8F0WjM7dr"
            },
            "source": [
                "## Star DeepChem on [GitHub](https://github.com/deepchem/deepchem)\n",
                "This helps build awareness of the DeepChem project and the tools for open source drug discovery that we're trying to build.\n",
                "\n",
                "## Join the DeepChem Discord\n",
                "The DeepChem [Discord](https://discord.gg/cGzwCdrUqS) hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "id": "dL-QWjr8NQZi"
            },
            "source": [
                "## Citing This Tutorial"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "id": "OuuI9QnhNYuu"
            },
            "source": [
                "If you found this tutorial useful please consider citing it using the provided BibTeX."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {
                "colab": {
                    "base_uri": "https://localhost:8080/",
                    "height": 106
                },
                "id": "doCbkAgzNVH1",
                "outputId": "4af97411-27ba-44b4-a48f-85a2fd7bf362"
            },
            "outputs": [],
            "source": [
                "@manual{Bioinformatics,\n",
                " title={Cell Counting Tutorial},\n",
                " organization={DeepChem},\n",
                " author={Menezes, Aaron},\n",
                " howpublished = {\\url{https://github.com/deepchem/deepchem/blob/master/examples/tutorials/Cell_Counting_Tutorial.ipynb}},\n",
                " year={2024},\n",
                "}"
            ]
        }
    ],
    "metadata": {
        "accelerator": "GPU",
        "colab": {
            "gpuType": "T4",
            "provenance": []
        },
        "kernelspec": {
            "display_name": "Python 3",
            "name": "python3"
        },
        "language_info": {
            "name": "python"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 0
}
